Multimodal 3D Genome Pre-training
Authors: Minghao Yang
Pengteng Li
Yan Liang
Qianyi Cai
Zhihang Zheng
Shichen Zhang
Pengfei ZHANG
Zhi-An Huang
Hui Xiong
Venue: NeurIPS 2025 poster ()
Keywords: 3D Genomics
Multimodal Foundation Model
Hi-C data
Chromatin Accessibility
Primary area: machine_learning_for_sciences
TL;DR: We propose the first multimodal foundation model for 3D genomics, integrating Hi-C contact maps and chromatin accessibility to achieve unified semantic representation, outperforming state-of-the-art methods on diverse tasks.

Abstract: Deep learning techniques have driven significant progress in various analytical tasks within 3D genomics in computational biology. However, a holistic understanding of 3D genomics knowledge remains underexplored. Here, we propose ***MIX-HIC***, the first multimodal foundation model of 3D genome that integrates both 3D genome structure and epigenomic tracks, which obtains unified and comprehensive semantics. For accurate heterogeneous semantic fusion, we design the cross-modal interaction and mapping blocks for robust unified representation, yielding the accurate aggregation of 3D genome knowledge. Besides, we introduce the first large-scale dataset comprising over ***1 million*** pairwise samples of Hi-C contact maps and epigenomic tracks for high-quality pre-training, enabling the exploration of functional implications in 3D genomics. Extensive experiments show that MIX-HIC significantly surpasses existing state-of-the-art methods in diverse downstream tasks. This work provides a valuable resource for advancing 3D genomics research.

Multimodal 3D Genome Pre-training
Minghao Yang', Pengteng Li", Yan Liang', Qianyi Cai Zhihang Zheng", Shichen Zhang', Pengfei
Zhang', Zhi-An Huang* and Hui Xiong!.."
Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), China
School of Artificial Intelligence, South China Normal University, China
Thrust of Bioscience and Biomedical Engineering, The Hong Kong University of Science and Technology (Guangzhou), China
*Department of Computer Science, City University of Hong Kong (Dongguan), China 'Department of Computer Science and Engineering, The Hong Kong University of Science and Technology Hong Kong SAR, China
Abstract
Deep learning techniques have driven significant progress in various analytical tasks within 3D genomics in computational biology. However, holistic understanding of genomics knowledge remains underexplored. Here, we propose MIX-HIC, the first multimodal foundation model of 3D genome that integrates both Hi-C contact maps and epigenomic tracks, which obtains unified and comprehensive semantics. For accurate heterogeneous semantic fusion, we design the cross-modal interaction and mapping blocks for robust unified representation, yielding the accurate aggregation of 3D genome knowledge. Besides, introduce the first large-scale dataset comprising over 1 million pairwise samples Hi-C contact maps and epigenomic tracks for high-quality pre-training, enabling the exploration of functional implications in 3D genomics. Extensive experiments show that MIX- HIC significantly surpasses existing state-of-the-a art methods in diverse downstream tasks. This work provides a valuable resource for advancing 3D genomics research.
Introduction
The stree-dimensiona (3D) organization of chromosomes within the nucleus plays a pivotal role in gene regulation and cellular function 21.2) Key topological features of the 3D genome, such as chromatin loops that bring distant regulatory elements into close physical proximity with their target genes, are essential for cell-type-specific transcriptional regulation. High-resolution 3D chromatin interactions can quantified through high-throughput chromosome conformation capture (Hi-C) technique 3] Understanding the mechanisms of how the 3D genome influences gene expression unveil pivotal insights into cellular functionality, developmental biology, and disease mechanisms
Recently, computational models have emerged as powerful tool to unravel the intricate associations between the 3D chromatin structure, epigenome, and transcriptome. Existing approaches predict various genomic features, including 3D chromatin contact maps [5] 6 7), chromatin loops [ [ 10]. and gene expression
[T] [2]. often leveraging DNA sequences, Hi-C contact maps and epigenomic tracks. Though successful, most of these methods are limited to a single specific task and struggle to integrate the diverse and heterogeneous information of the 3D genome, hindering comprehensive understanding of its complex organization.
Recent progress large-scale foundation models has demonstrated remarkable success in various fields of computational biology, such as molecular representations [3 [4], medical imaging [5 [6].
proteomics [17 18) and genomies [9 20 Inspired by these advancements, we aim develop
"Correspondence to Zhi-An Huang zhuang.a@cciyu-dde.cdu.oom Hui Xiong exinngggihhghhi.s..hh. The source code of MIX-HIC is available at
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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multimodal foundation model to address the downstream tasks in isolation.
limitations of analyzing 3D genomic
Modeling the multimodal foundation model of 3D genome introduces three key challenges. First, contact maps and epigenomic tracks have inherently distinct characteristics, making integration difficult. Simply aligning features from the two modalities and projecting them into a unified latent space would primarily capture modal-invariant knowledge like gene regulatory mechanisms, which are governed by both chromatin spatial organization and epigenomic tracks. However, this approach tends to overlook modal-specific characteristics, such as precise chemical modifications and chromatin states revealed by epigenomic tracks, which are essential factors for fine- aee-ggained 3D genome analysis. This can lead to information loss and degrade downstream task performance (Refer to A theoretical analysis). Second, the unified representation from heterogeneous 3D genomic and epigenomic data must exhibit robust generalization capabilities; otherwise, the model may struggle to adapt effectively to diverse downstream tasks e.g., generation and regression. Third, uncovering implicit semantic relationships between 3D genomic and epigenomic data is crucial for addressing the data scarcity problem in 3D genomics. Especially, the high experimental costs of Hi-C sequencing in real-world applications limit data accessibility, leading to incomplete representations and degraded model performance. Pre-training model to learn implicit multimodal structural connections offers significant benefits for downstream tasks, particularly when only singl-moddlity data is available. This enables the model to leverage structural knowledge to compensate for missing modality semantics, thereby enriching the overall data representation.
Hence, we introduce MIX-HIC, the first multimodal foundation model for 3D genomics to extract fine-grained knowledge from 3D genome and epigenomic tracks, enabling efficient adaptation to diverse downstream tasks with superior performance. Specifically, MIX-HIC first incorporates two distinct encoders to capture the refined features from 3D genome contact maps and epigenomic tracks, leveraging cell type-specific information for accurate predictions in novel cell types. To address the challenge of integrating heterogeneous data, we propose a cross-modal interaction block to capture both modal-invariant and modal-specific representations, regularized by contrastive learning and orthogonal constraints, preserving both shared and distinctive information across modalities. Additionally, cross modal mapping block facilitates information exchange between modalities, ensuring robust representation even with single-mon modaalty input. To comprchensively capture 3D genome knowledge, have curated large-scale dataset that consists of 1, 275,948 pair samples of Hi-C contact maps and epigenomic tracks for rapid adaptation to downstream tasks through task-specific decoders. Notably, this is the largest paired dataset for the 3D genome analysis to date. MIX-HIC is evaluated across diverse downstream tasks, demonstrating its effectiveness and robustness in comparison to other state-of-the-ar methods. In summary, the main contributions are:
We propose the first 3D genomic multimodal foundation model, integrating Hi-C contact maps and epigenomic tracks to establish new paradigm for 3D genome analysis.
foutiaatioooono
MIX-HI HIC features novel architecture with two key components: (1) a cross- modal interaction block to capture both shared and unique biological patterns across modalities; and (2) cross-modal mapping block to enable a reliable complement of missing modality features.
For holistic representation learning in 3D genome analysis, we present the largest paired dataset of Hi-C and epigenomic tracks, comprising over million samples.
Extensive experiments demonstrate that MIX-HIC achieves state-of-the-art performance on three critical downstream tasks across two cell lines.
2 Related Works
2.1
3D Genome-Related Tasks
This work evaluates the effectiveness of MIX-HIC on three downstream tasks, including Hi-C contact map prediction, chromatin loop detection, and CAGE-seq expression prediction.
Existing methods for Hi-C contact map prediction from DNA sequences show promise but lack cross-cell-type generalization 21. Models like EPCOT [1] and C.Origami (6) improve this by
integrating DNA sequence with cell-type-specifie epigenomic tracks. Epiphany [ offers a more efficient solution using only the epigenomic tracks.

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Chromatin loop detection methods are broadly divided into statistical and supervised learning methods. Statistical methods like HiCExplorer [22] and ChromoSight (9] rely on contact frequency distributions or expert-defined templates to identify loops. Supervised learning methods, including Peakachu [8] DLoopCaller [10] leverage labeled data and sophisticated architectures for loop detection. RefHiC [23] employs coarse-to-fine training strategy that integrates multi-resolution Hi-C contact maps through contrastive learning mechanisms for model pre-training However, RefHiC is limited by its reliance on small-scale data semanties, which hinders its generalizability to other downstream tasks.
CAGE-sep seq expression prediction typically uses multimodal inputs, including DNA sequences, epige- nomic tracks, and Hi-C contact maps. Enformer [24] employs transformers for modeling DNA sequences, while EPCOT [T]] integrates DNA sequences and epigenomic tracks with transformers or long short-term memory (LSTM) (25]. GraphReg [12] integrates epigenomic tracks and Hi-C contact maps using graph attention networks for expression prediction.
Despite their notable achievements, these methods often exhibit limitations in knowledge transfer across tasks and fail to fully capture the complex interaction patterns between multimodal data.
2.2 Foundation Models in Computational Biology
The emergence of foundation models has significantly advanced various fields of computational biol- ogy. For example, EvoRank (26) has demonstrated remarkable capabilities by harnessing extensive protein sequence datasets to derive latent representations through sequence alignment. VQDNA [19] employs large-scale DNA sequences to learn adaptive tokenization through vector-quantized code- books. To capture more comprehensive views of data, multimodal foundation models such as ESM-IF 277 combine protein sequences and 3D structures to learn functional and structural representations, advancing protein function design and prediction. Similarly, UniCorn [28] integrates 2D and 3D molecular views through contrastive learning, effectively capturing complementary bimodal features. These foundation models have showcased strong representation learning capabilities of foundation models, inspiring the development of universal model capable of effectively addressing various downstream tasks in the 3D genomic field.
3 Data Processing and Preparation
To distill the comprehensive semantics from the 3D genome, we collect and refine large- scale dataset for pre-training MIX-HIC, using publicly available data from the hg38 assem- bly. The Hi-C contact maps are obtained from the 4DN Data Portal" while the epigenomic tracks (ATAC-seq and DNase-seq, which mea- sure how 'open' or accessible DNA is for tran- scription), CAGFE-se expression data (which di- rectly quantifies gene activity levels), and CTCF
Table 1: Summary of pre-training data for MIX- HIC, including original and cleaned data counts.
Cell line
Original
Cleaned
Remain
HepG2 HCT116
48, 415
38, 536
79.5%
276, 384
189, 099
68.4%
IMR90
471, 557
316, 794
67.2%
WTC11
1, 032, 048
731, 519
70.9%
Total
1, 828, 401
1, 275,, 948
69.8%
ChIA-PET (29) chromatin loops (which identify high-confidence interactions mediated by the key architectural protein CTCF) are downloaded from the ENCODE Portal? ? Due to the high cost of deep sequencing for Hi-C experiments (30), publicly available datasets are typically limited to resolutions of 5 kb, 10 kb, or coarser. 5 kb resolution is a fine-grained and effective choice for deep learning models [ [2]. The MIX-HIC model processes 250,000 base pair (bp) genomic windows, a size selected to encompass key regulatory structures like chromatin loops, ensuring most functional units are fully contained within the inputs 55 g) This results in Hi-C contact maps being represented as 50 50 matrices at the kb resolution. To reduce data variability, epigenomic tracks are averaged over every 100 bps, which generates 2,500-length sequences. Each bin in the Hi-C matrix corresponds to specific x and coordinates, representing two distinct genomic segments. Their respective epigenomic sequences 5,000-length representation for that bin. Thus, Hi-C contact maps and epigenomic tracks are treated as images and sequences, respectively. We focus four cell lines for pre-training, including HepG2, HCT116, IMR90, and WTC111. To ensure data quality, we filter out windows with fewer than 10% non-zero Hi-C interactions insufficient contact signals, which is standard quality control step to remove uninformative data g [0]. Hi-C contact maps are inherently sparse, especially for
concatenated into
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3

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Feature Extrattion
Cross-medal Interaction
Cross-modal Mapping
Contect Map
Mapping
X
Looth
Lincar
X G
-XarzE
X
Troo
lenagying
Epggeoom
Xg
x
-XieM
0
X2
Touth
Mappinz
Figure 1: Pre-training stage of MIX-HIC. MIX-HIC employs a dual-encoder architecture to extract refined features from both Hi-C contact maps and epigenomic tracks. The modal-specifie and modal- invariant representations are learned via contrastive learning and orthogonal constraints within the cross-modal interaction block. A cross-modal mapping block is developed to further regularize the bimodal representations and facilitate cross-modal complement. ΦM and ΦR denote the Hi-C contact map encoder and epigenomic track encoder, respectively.
long-range interactions distant from the diagonal. Including these extremely sparse, low-signal windows would introduce noise and degrade the model's training process. As summarized in Table [ this filtering process removed approximately 30% of the raw windows. Crucially, massive and high-quality dataset of over 1.2 million sample pairs was retained, which is more than sufficient for robust pre-training. Further details on data processing, reference number, and downstream task data provided in Appendix A.7
Methodology
4.1 Self-supervised Pre-training
Self-supervised learning B1 32 offers powerful solution for learning unified and comprehensive representations from heterogeneous 3D genomic and epigenomic data. By leveraging large-scale pairwise data during pre-training. MIX-HIC effectively captures inherent biological patterns and relationships across these bimodal data, significantly enhancing its adaptability to diverse downstream tasks. As shown in Figure TT. the pre-training phase includes feature extraction block, cross- modal interaction block, and cross-modal mapping block. The feature extraction block employs specialized encoders for epigenomic tracks and Hi-C contact maps, respectively, to generate refined representations. The learned representations are then fed into the cross-modal interaction block to learn both modal-specific and modal-invariant features. This learning process is regularized through combination of contrastive learning loss and orthogonal loss. Finally, cross-modal mapping block further explores latent connections and complementary information between the two modalities.
Feature extraction block. The feature extraction block consists of Hi-C and epigenomic feature encoders. Given the Transformer's 's capability to model long-range dependencies and its flexibility in capturing both spatial interactions (Hi-C contact maps) and sequential relationships (epigenomic tracks), we utilize a Transformer-baset architecture for robust feature extraction and multimodal integration. Similar to Vision Transformer (ViT) (34), the single-channel Hi-C contact map X M E RIxHxW is first transformed into sequence of flattened patches X E NXD Here, H and W are the height and width of the Hi-C contact map, both equal to 50. The dimension D = 1 P x P corresponds to the initial size each patch, where the patch size P is set to 2. The total number of these patches N H x W/p² is the resulting number of patches, which becomes the input sequence length. In the Hi-C feature encoder, a feedforward network projects these patches into embedding X E NCC where C denotes the predefined feature dimension. This embedding then refined through three cascaded encoder layers, where each layer consists of T Transformer blocks followed by downsampling layer, yielding progressively refined embeddings X E Ro. xC; where the sequence length a; is defined a; ş and the feature dimension C: grows exponentially C; 2'C', with the encoder layer {1, 3, Finally, a bottleneck layer equipped with T Transformer blocks is applied to produce the Hi-C contact map embedding X & E x
The epigenomic encoder processes input sequences X E R44x where I. = 5. 000 is the initial sequence length, and O represents the two epigenomic tracks: ATAC-seq and DNase-seq.

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Convolutional layers with max pooling operations extract an initial embedding X E R where L2 100 corresponds to two genomic segments along both z and axes in the Hi-C contact map. Note that processing epigenomic tracks at high resolution (e-g., 100bp) is common practice to avoid over-smoothing [5] III. Similarly, the embedding X is processed through three encoder layers, where each layer consists of T Transformer blocks followed by downsampling layer, resulting in L/2 for i (1. Then, the refined epigenomic representation X 2 is also derived from final bottleneck layer.
X E R xC where the sequence length B; = 2
Cross-modal interaction block. Conventional multimodal learning architectures [35] often utilize contrastive learning to project features from different modalities into a shared embedding space. However, these modalities inherently contain both homogeneous and heterogeneous information. Di- rect aligning all features risks losing essential downstream task performance (7). This further analyzed Theorem as follows.
characteristics, potentially diminishing
Theorem 1. Let Φ1 and 02 be feature encoders for two modalities 21 and 22, respectively. If the encoded features F (21) and F2 (22)2) are perfectly aligned such that F = F2, we have:
inf (h F²), 0) inf B. (M/ (N/ (21, 22), h
(I)
Remarks. The information gap r == max m (=1 (2) - -min t), (22; t)} quantifies the effectiveness of the modalities in predicting target variable t, where U (22; t) represents the mutual information. Here, E represents the expectation, q denotes the joint distribution of (21, 1), Lce is the cross-entropy loss, and h and J are prediction functions for features and raw data, respectively. Theorem demonstrates that perfect alignment results in prediction errors that are suboptimal by least I compared to using raw modalities directly. This information gap widens when information content is imbalanced across modalities. The complete proof is provided in Appendix
To address this, our cross-modal interaction block captures both modal-specific and modal-invariant representations, enabling more comprehensive understanding of the data. Specifically, employ four independent dense networks to process the Hi-C contact map representation X and the epigenomic representation x This generates the condensed modal-invarian representations X1 E R2sxxC2 and X E RS as well as the modal-specific representations X E RasxCa and X E RR The mean pooling operation is then applied along the sequence length dimension, producing X/. X X and X each with feature dimension of C2. To ensure that the modal- invariant features capture shared knowledge across modalities, we incorporate a contrastive learning loss regularize these representations. We propose unified contrastive loss function Luair 31 to compute the similarity between two modalities A and as follows:
B,)/T Cpais (A, -- log D exp (A₁, B.)/T
exp(A,,B,)/T
where denotes the temperature, commonly set to 0.07
38 J refers the hatch size, .) is the dot product operation, and A, and B, represent the embedding of the j-th sample in the mini-batch for modality A and B, respectively. Finally, the overall contrastive loss is computed follows:
Leon 2 (Cpuie (X X/1) + Guii (X X())
(3)
Moreover, we introduce an orthogonal constraint maximize the dissimilarity between modal- specific and modal-invariant features. This ensures that the modal-specific features capture comple- mentary information distinct from the shared knowledge represented by the modal-invariant features. The orthogonal loss Corth is calculated by minimizing the inner product between these features:
Lonh
(x M X (X (2))
Cross-modal mapping block. Data scarcity, often due to high experimental costs, can lead to incomplete datasets with missing modalities. Integrating predicted features a missing modality with existing modalities can enhance prediction performance (37 39 Our cross-modal mapping block aims to capture the implicit semantic relationships and facilitate knowledge transfer between modalities to address this issuc.
5

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Some downstream tasks require the MIX-HIC to preserve the sequence length of the input features. However, the differing lengths of Hi-C contact maps and epigenomic tracks pose a challenge for effec- tive modality transfer. Therefore, we apply ID adaptive pooling to the concatenated representations of Hi-C. XCConar {x X E RosxCaa and epigenomic tracks, & = [ x E R& xCs
(where [ -] denotes concatenation), to align their lengths. This yields the complementary representa- tions M22 E IR f2 x and XEIM E C as follows:
M2E = F2E (9M2E( M )), XE2M = FE2M (XE
(5)
where GMZE and GFOM are ID adaptive pooling operations. FMIE and FFOM denote dense layers with the same output dimensions matching their inputs. To ensure these mapped embeddings capture the relevant information from the target modality, we use a Lmmapping loss for regularization:
mapping
[[l XMDE - Xg
Comneut 1/
X|
XM
(6)
Overall, the final loss for self- sulevvupe pre-training is computed as follows:
Lpeetmin = Leon + Loth + Lemppping
4.2 Task-specific Fine-tuning
As depicted in Figure 2 MIX-HIC a versatile framework capable of processing various kinds of inputs. Ideally, MIX-H HIC takes both the Hi-C contact map and the epigenomic feature profile as inputs (represented as HiX--ol-Bimoda)) extracting their concatenated features XCoocatt and Ceecatt similar to the feature extraction block used in the self--upprrvied pre-training stage. However, in real-world scenarios, a certain modality may be absent for various unforeseen reasons.
Leveraging the powerful representation ability of pre-training to capture implicit connections between bimodal data, MIX-HIC incorporates cross-modal mapping block to complement the features of the missing modality using the information from the available modality. For ex- ample. when the Hi-C contact map is missing, MIX-HIC can infer the missing modality fea-
X
Taboopclkc Deoolar
Dremircan Tadla
BSSCC Canac Map OMtl-et--till
Mopia
Ch Lees (MIs-lC--Brodd.)
CACEE Eiqeedax (MIX (icoBi
tures XPM from the concatenated epigenomic embedding g using Eq. 5] For the Hi-C contact map prediction task, even if only the epigenomic tracks are available, the correspond- ing contact map features XEZM can still be uti- lized for prediction (denoted as MIX-HIC-Infer).
Figure 2: Fine-tuning stage of MIX-HIC. Epige- nomic features are captured from the pre-trained encoder, while Hi-C contact map features are ob- tained either directly from the pre-trained encoder or through feature mapping based on the epige- nomic features. MIX-HIC incorporates a modality fusion block to integrate the bimodal representa- tions, followed by task-specific decoder for final predictions of downstream tasks.
Modality-fusioe block. We employ T stacked contact maa-groundedd fusion blocks to learn the interaction patterns between the bimodal repre- sentations. Each contact map-grounded fusion block consists of a self-attention layer, a cross-attention layer, and a feedforward network. The extracted epigenomic embeddings X X are first input into the self-attention layer, generating query embeddings of the cross-attention layer. The contact map embeddings, either X or XEIM. serve as the key and value embeddings, which are then processed through the cross-attention layer. Finally, a feedforward neural network applied to produce the fusion embeddings Xruse E 12 xxxx
Task-specific prediction block. Three types of decoders are involved in the task-specific prediction block. The chromatin loop detection task is a binary classification problem. Therefore, the decoder for this task includes mean pooling operation along the sequence length axis, followed by feedforward network, to classify a given sample as either a chromatin loop or a non-loop.
The CAGE-seq expression task is formulated as a regression problem, aiming to predict 100 values corresponding the expression levels of two genomic segments (each of length 50) along z-axis and y-axis of the Hi-C contact map. The decoder consists of three transformer blocks, with each followed by an upsampling layer. At each stage, the encoder-derived features X from the i-th encoder layer are concatenated with the corresponding i-th layer of decoder outputs X from the

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preceding stage via skip connections, facilitating feature integration across network depths. Finally, the decoder output features Xuu E 1/2xx0 are processed through two feedforward networks to generate regression predictions.
To predict the 50 50 Hi-C contact maps at 5kh resolution, the decoder output features Xomi obtained similar to the CAGE-seq expression task. These features are then split into two feature segments X E |5002C and X |250x2 along the length axis. X and X are unsqueezed into R550x x2C and 1 respectively, after which element- wise addition and multiplication operations are applied to generate feature maps of size x Finally, two layers of feedforward networks are utilized predict the Hi-C contact maps.
Training loss. The chromatin loop detection task employs the binary eross entropy (BCE) loss function, while the Hi-C contact map prediction and CAGE-seq expression prediction tasks use the mean squared error (MSE) loss function. should be noted that we normalize CAGE-seq data using RPGC [40] and Hi-C data using KR [41] normalization correct for sequencing depth and systematic biases, respectively. Subsequently, a log transformation is applied to both datasets. This is critical for stabilizing variance and compressing the highly skewed distribution of raw counts, ultimately producing continuous values representing normalized interaction frequencies. Consequently, the MSE loss becomes an appropriate and robust choice for these continuous values. The MSE and BSE loss functions can be formulated as follows:
1
LMSE
Σ j=1
11 - 0,)
(8)
1
LBCE
Σ
yy
()) + (1 - y,) log(1 ú,]],
(9)
where y; and i represent the true value and the predicted value, respectively.
5 Experiments
Implementation details, hyperparameter analysis, and analyses of the model's 's biological grounding and robustness for noise can be referred to Appendix.
5.1
Comparison Results on Downstream Tasks
To demonstrate the effectiveness MIX-HIC, three downstream tasks are involved in this work, in- cluding Hi-C contact map prediction, chromatin loop detection, and CAGE--eq expression prediction. We construct three versions of MIX- (1) MIX-HIC HIC-Bimodal, which leverages both Hi-C contact maps and epigenomic tracks through pre- treiniinn (2) MIX-HIC-Non
a non -reerrrtdd version using the same bimodal inputs; and (3) MIX-HIC-Infer, designed to handle missing Hi-C data by integrating epigenomic track embeddings with inferred Hi-C embeddings. Detailed descriptions of the compared state-of-the-ee methods are given Appendix D
3D Chromatin Organization Prediction. In this task, the contact maps serve as the target. MIX- HIC-Infer is compared with four state-of-the-art methods, including Epiphany [ C.Origami (
Table 2: Methods comparison for the Hi-C contact map prediction task on GM12878 and K562 cell lines using R The results marked in bold and underlined denote the best and second-best performing methods, respectively.
and two variants of EPCOT 11.. (i.e. EPCOT-
LSTM and EPCOT-Transformer)) For a fair com- parison, all methods receive the same input MIX- HIC and are configured with their default parameter settings. The coefficient of determination (R²) [42 is employed as the evaluation metric, since it effec- tively quantifies explained variance in analyses of long-tailed or sparse data, such as low-probability long-range interactions in Hi-C.
Methods
GM12878
K562
Epiphany
0.7970
0.6547
C.Origami
0.7958
0,7055
EPCOT- Transformer
11
0.5409
0,7648
EPCOT-LSTM [
0.7993
0,7840
MIX Infer (Ours)
0.8724
9.85%
0.8001
12.1%
Table 2 shows the evaluation performance on GM12878 and K562 cell lines. Compared other methods, MIX-HIC-Ine Infer demonstrates superior performance, achieving the highest average R² values on both GM 1278 and K562 cell lines. Specifically, it outperforms the runner-up method by approximately 9.3% and 2.1 % in R22 score on GM12878 and K562, respectively. We note that the EPCOT variants deliver competitive performance, but they suffer from fluctuations across the two datasets. Through extensive pre-training large-scale pairwise datasets, MIX-HIC efffectiveyy
7

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Table 3: Methods comparison for the supervised chromatin loop detection task GM12878 and K562 cell lines.
GM12266
K562
Methods
Procision
Recall
F1
AUROCC
Precision
Recall
F1
AUROC
Peakachu
[
0,7763
0.8263
0,8015
0.8766
0.7895
0.7905
0,7900
0,8834
copCaller 1D
0.8433
0,8075
0.9046
0,8383
0.7526
0.7932
MIX-HIC-BB HiC--Binnddlll (Ours)
0.8505...
0.8337 -0.715
0.8420 22.55
0.9209
0,8521,
0.8027
0.8367
0.9194
explores implicit semantic relationships between bimodal data, enabling robust compensation for missing modality semantics and achieving promising accuracy in Hi-C contact map prediction.
Chromatin Loop Detection. Two supervised machine learning- based methods DI DloopCaller [10] and Peakachu [8] used to compare with MIX-HIC-Bimodal. As illustrated in Table 3 MIX- HIC-Bimodal surpasses other machine learning- beamiingbbssed methods across all classification metrics on two datasets. By integrating both epigenomic tracks and Hi-C contact maps, DLoopCaller slightly outperforms Peakachu, which relies solely on Hi-C contact maps. The enhanced performance of MIX-HIC can be attributed the effective self-supervised pre-training, which facilitates the capture of both the simple concatenation employed by DLoopCaller
and modal-specific information from bimodal representations, in contrast to
Supported loops vs. Predicted loops
We employ both cutisticallbasee methods, namely ChromoSight [9] and HiCExplorer [22], as well as machine learning-basee methods to annotate loops across entire chromosomes. The predicted loops are further compared with those loops validated by experimental data from ChIA-PET. Method details
5000
HIC-HIC Simodal (Ours)
HiCZsploor
Nambew suppoted looe
DLoopCaler
3000
for whhoee-cromosooee chromatin loop annotation and corresponding results with the quantitative 'pro- portion' metric are available in Appendix [A.2 and
2000
1000
C.1 respectively. Figure 3 compares the number of predicted loops versus the number of loops sup- ported by ChIA-PET among various methods on the GM12878 dataset. Among all the methods, machine learning- methods generally predict higher number of loops that are validated by ChIA-PET on the GM12878 dataset, with MIX-HIC identifying the most ChIA-PET validated loops while maintaining the highest validated proportion. On the K362 dataset, which contains much less training data compared to GM12878, the efficacy of machine learning methods generally declines. Nevertheless, MIX-HIC consistently maintains the highest proportion of validated loops in comparison with other methods. An example of chhole-crroosomee chromatin loop comparison is provided in Appendix C.2
01
0
1000
2000
3000
6000
5000
6000
7000
Number predicted loops
Figure 3: Comparison of the number of pre- dicted loops with the number of correspond- ing ChIA-PET- PET-supported loops across various deep learning methods on GM12878 cell line.
CAGE-seq Expression Prediction. MIC-
Table 4: Methods comparison for the CAGE- seq expression prediction task on GM12878 and K562 cell lines using R².
Bimodal is pitted against four benchmark methods:
two variants of GraphReg (EPI-CNN and EPI-Graph)
12]. as well as two variants of EPCOT (EPCOT- LSTM and EPCCTT-Tranfformet [T. EPI-CNN re- lies solely on epigenomic tracks to predict CAGE-seq expression, whereas EPI-Graph enhances this by in- corporating Hi-C contact maps via graph attention net- works for dual-modal modeling. Table showcases the comparison results across two datasets. EPCOT achieves competitive performance, yet it suffers from a time-consuming problem due to the process long-range DNA sequence. MIX-HIC demonstrates remarkable performance over other benchmark methods across all metrics on both datasets.
Methods
GM12878
K562
EPI-CNN
0.7719
0.8033
EPI-Graph
0.7965
0.8211
EPCOT-LSTM
0.8704
0.8230
HIIC--BBBinaaal (Ours)
.3.0%
0.9077 -+077..4..89
Overall, MIX-HIC outperforms state-of-the-art methods across all downstream tasks, with the most significant improvement (9.3% R²) in Hi-C contact map prediction. Large-scale pre-training enables
8

---

robust semantic representations for MIX-HIC while other methods lack generalizability due to being narrowly optimized for specific tasks and often show inconsistent performance across datasets.
5.2 Few-shot Chromatin Loops Classification
We conduct few-shot learning experiment on the chromatin loops classification task to evaluate the performance of MIX-HIC under limited training data scenarios. Specifically, we fine-tune MIX- HIC-Bimodal, MIX-HIC-NonPre,, DloopCaller, and Peakachu using four different ratios of the training data, ranging from 0.0001 to 0.1. As illustrated in Fig- ure 4 TIX-HIC-Bimodee
CloopCaler
Paskach
CNT12596
AUR00
AUBOC
0.01
00001
0.301
2.01
011
consistently outperforms other methods across all data ratios. Notably, the per- formance of most methods remains relatively robust even with minimal data, primarily stemming from the powerful and repetitive biological signatures of chro- matin loops, which make the task tractable for most models. The performance variations among different architectures also highlight their inherent data efficiency. For instance, Peakachu, random forest model, is inherently stable as it relies on engineered features that are less sensitive to data volume. In contrast, DioopCaller's CNN architecture more data-hungry and thus shows steeper decline. Our MIX-HIC architecture, even without pre-training, proves more data-efficient: the self-altention mechanism is better suited for capturing the global and long-range dependencies in contact maps than local CNNs, and its bimodal input provides complementary information, enhancing robustness even low-data regimes.
Figure 4: Few-shot chromatin loop classifi- cation performance across different training data ratios. Mean values and standard errors calculated over five independent runs with varying random seeds.
With a training data ratio of O. MIIX-HC--iimen achieves an AUROC of about 0.9 on two datasets, which competitive with other state-of-thee methods trained on full datasets. These findings highlight the robustness and efficiency of MIX-HIC in leveraging pre-trained knowledge to achieve superior performance even with limited labels.
5.3 Robust Performance Across Cell Types
The utilization of cell-type specific data, i.e., Hi-C contact maps and epigenomic tracks, empowers MIX- HIC achieve accurate predictions for novel cell types. We perform a cross-dataset evaluation with the GM12878 and K562 cell lines the chromatin loop detection task to assess the generalization ability of MIX-HIC. In particular, all the models are trained on one cell line and evaluated on the other. The evalua- tion results of cross-cell type prediction are shown 5 We note decrease in performance when all models are evaluated on external datasets compared to within-datased testing (see Table Neverthe- less, MEX-HIC-Bimode continues to outperform other methods, indicating its strong generalization ability in real-world scenarios.
CMIGETE
Treined K3G Tinted
0.00
85531
08232
0.
MDN Beanne
Pheaasto
Padaddu
Las Hec
Figure 5: Methods comparison for cross-cell- type evaluation.
5.4 Ablation Study
To evaluate the effectiveness of key components in MIX-HIC, we perform ablation studies focus- ing on the proposed loss terms and the represen- tation learning ability across multimodal data.
Table 5: AUROC results of ablation studies on loss terms for chromatin loop detection task. Symbols ' and '_' denote present and absent, respectively.
Leon Lonh
Lmappiin
GM12878
K562
Three critical loss terms are employed during MIX-HIC pre-training (i.e., Leons Lorth, and fompping). We examine the effects of each loss component. As shown in Table 5 the model equipped with Lcon provides strong bascline.
0.9136
0.9099
v
0.9183
0.9156
v
v
0.9209
0.9194

---

The orthogonal loss enhances discrimination between modal invariant and modal-specific represen- tations, contributing about 0.5% AUROC improvement compared to simply feature alignment. as demonstrated in Theorem 1. Further details for the orthogonal constaini are provided in Appendix C.4 Although the cross- modal mapping loss provides modest enhancement, it enables missing modality inference of MIX-HIC. These findings demonstrate that robust multimodal fusion is achieved by explicitly separating modal-invariant and modal-specific representations using appropriate loss terms.
In addition, we assess the representation learn- ing ability of MIX-HIC across three aspects: the contribution of pre-training, the superiority of multimodal over unimodal representations, and the efficiency of modality completion. For the Hi-C contact map prediction task, the model is trained to predict the contact map using only ID epigenomic tracks as input. The Hi-C con- tact map itself is the prediction target (output), making its use as an input feature methodolog- ically invalid. Detailed comparison results ap- pear in Table O The pre-trained bimodal MIX- HIC achieves superior performance compared to its non-pre con-pre-trained counterpart. confirming pre-training's 's benefit for unified representations. Moreover, while pre-trained bimodal MIX-HIC consistently outperforms single-modal variants,
Table Ablation results of each modality, with values reported as R (Hi-C contact map predic- tion), AUROC (chromatin loops dectection), and R (CAGE-seq expression prediction). Symbol 'o' represents inferred embeddings from the other modality.
Tiaks
Epi.
Hi-C
Pre-iainee
GM12258
KS62
oup proliction
0.7709
0,8001
0.8236
0.8054
0.3226
0
Chromatio loops dectection
0.9135
0.9199
0.3699
0.9194
IL8514
0.3710
CACiF-egg exprossion prodiction
018684
0,3670
0.5833
0.9077
the non non-pre-trained bimodal version underperforms compared to using Hi-C data alone for K562
chromatin loop detection due to heterogeneity between bimodal data, highlighting the importance of learning both modality-specific information and general patterns. Finally, when Hi-C data is un- available, combining epigenomic embeddings with inferred Hi-C embeddings improves performance over using epigenomic data alone, validating the cross-modal modal mapping block's 's effectiveness for data scarcity scenarios.
6 Conclusion
In this work, we present MIX-HIC, a novel multimodal foundation model for diverse 3D genome downstream tasks by integrating Hi-C contact maps and epigenomic tracks. To facilitate unified and comprehensive understanding of 3D genome organization, we construct the largest paired 3D genome dataset to date, comprising over one million high-quality samples. As the first multimodal foundation model in this domain, MIX-HIC incorporates two key innovations: (1) a cross cross-modal interaction block that jointly learns modal-invariant and modal-specific representations, effectively capturing both shared and unique biological patterns across modalities; and (2) a cross-modal mapping block that regularizes the multimodal feature space and enables robust imputation of missing modalities features, alleviating the practical challenges posed by the high costs of Hi-C contact map acquisition. Comprehensive experimental results demonstrate that MIX- HIC achieves state-of of-the-art performance on three key downstream tasks across two cell lines.
7 Acknowledgments
This work was supported in part by the National Key R&D Program of China (Grant No.2023YFFO725001,, in part by the National Natural Science Foundation of China (Grant Nos. N03,9237,204, 62572413), in part by the guangdong Basic and Applied Basic Research Foun- dation (Grant Nos.202331511510005 2t1.:/55522222772222221555522220 and in part by the Education Bureau of Guangzhou.
2025A15150112944)
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A Implementation Details.
In this section, we present the implementation details of MIX-HIC, including data source and processing, whole-chromosome loop annotation, as well as experimental settings.
Table 7: Data sources and accession numbers from 4DN Data Portal and ENCODE Data Portal.
Cell line
Hi-C
DNasc-sey
ATAC-seq
ChIA-PET
CAGE-seq
GM12878
4DNFLIUEGIHD
ENCSR000EMT
ENCSR09SONB
ENCSRI84YZV
ENCSRO0OCKA ENCSR00OCIN
K562
4DNFITUOMFUQ
ENCSR000EOT
ENCSR956DNB
ENCSR597AKG
HepG2
4DNFICSTCIOZ
ENCSRO0OEJV
ENCSR042AWH
ENCSR41
HCT116
4DNFIXTASÓHE:
ENCSROUOENM
ENCSR872WGW
ENCSR2781ZK
IMR90
4DNFIH7TH4MF
ENCSR477RTP
ENCSR2000ML
ENCSR076TTY
WTC11
4DNFIVSCH2CH
ENCSR785ZUI
ENCSR54IKFY
ENC5R353ASS
A.1
Data Source and Processing
Detailed reference numbers for all data sources are provided in Table 7 We download the BAM files for epigenomic tracks and convert them into bigWig files using deepTools 440 with RPGC normalization. Two 250 kb epigenomic regions, corresponding to the x-axis and y-axis of a Hi-C contact map, are first averaged over every 100 bp and transformed using log(ar 1) reduce data variability, and then the processed regions are concatenated to form the final epigenomic tracks imput with a length of 5,000. The Hi-C contact maps are binned at 5 kb resolution and normalized using KR normalization, and then divided into 50 x 50 sub-matrices based on the loci of each sample.
As the first attempt create the multimodal 3D genome foundation model, we prioritize epigenomic tracks with ATAC-seq and DNase-seq because they capture the genome's 's foundational regulatory information. While DNase-seq and ATAC-seq have some inherent redundancy, they also offer complementary insights due to their distinct enzymatic biases. The enzymes' different cutting biases cause each assay detect unique accessible sites, which together yield more complete accessibility landscape 43
The training samples of three downstream tasks generated using two widely adopted cell lines, GM12878 and K562, as described below. First, the Hi-C contact maps and epigenomic tracks within a 250 kb genomic region upstream and downstream of the gene transcription start site (TSS) are used to predict CAGE-seq expression, with TSS annotations obtained from previous work [12] For this task, the number of training samples for GM12878 and K562 are 16,046 and 14,739, respectively. Second, the same pairwise samples of Hi-C contact maps and epigenomic tracks are used for the Hi-C contact map prediction task, with the same sample sizes as mentioned above. Third, the positive chromatin loops (1,344,770 for GM12877 and 137,55t for K562) are derived from the ChIA-PET data, while equal number of negative loops are randomly sampled based two criteria following prior research [1O]: (1) matching the distance distribution of positive samples using the distances probability density function, and (2) selecting interactions with distances greater than the maximum distance observed in the positive samples to enhance the diversity of the negative samples, Following previous work [T0 6 5l, we partition the chromosomes into distinct training, validation, and test sets. Specifically, chromosomes 10 and 11 serve as the validation set, chromosomes 3, 13, and 17 as the test set, and the remaining chromosomes are used for model training across three downstream tasks.
A.2 Whole-chromosome Loops Annotation
The well-trained MIX-HIC model is capable of predicting all potential chromatin interactions across individual chromosomes. The detection of chromatin loops within each chromosome consists of two key steps: (1) scoring potential chromatin interactions using MIX-HIC and (2) aggregating loops through a clustering algorithm. First, candidate elements are identified by examining the diagonals of the raw Hi-C contact matrix, where observed interaction frequencies are statistically compared to expected values based on Poisson distribution, retaining only elements with p-values below 0.01. Subsequently, the trained MIX-HIC model is employed to predict loop probability scores, with scores exceeding 0.5 referred to as candidate loops. These candidates are further grouped identify significant loops using density- based clustering algorithm following 44). For each candidate,
15

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local density score and a minimum distance metric are computed. Candidates exhibiting low distance values are subsequently eliminated minimize redundancy. Cluster centers are determined via target-decoy search, ensuring that the false discovery rate remains below 5%. Within each cluster, the candidate demonstrating the highest local density is designated as the representative loop.
A.3 Experimental Settings
MIX-HIC is developed using Python and PyTorch, and executed on the Ubuntu platform with Tesla A100 GPU. MIX-HIC processes input data consisting of 50 x 50 Hi-C contact maps and epigenomic tracks with sequence length of 5,000 bps. 'he architecture is composed of feature encoders, task-specific decoders, and a modality fusion block. The feature encoders and task-specific decoders are structured with four layers, each comprising two transformer encoder blocks. Notably, the epigenomic feature encoder includes an additional preprocessing stage, integrating four convolutional layers followed by max- pooling operations before the transformer blocks, to effectively process and condense long sequences. Similarly, the modality fusion block is constructed with two transformer encoder blocks, ensuring efficient integration of features across modalities.
During the pre-training stage, MIX-HIC is configured with 500 epochs, a learning rate of 1e-5, and a batch size of 256. For the CAGE-seq expression prediction task, the predefined feature dimension C' and learning rate are set to 256 and le-4, respectively. For the prediction of Hi-C contact maps and chromatin loops, these parameters are specified 128 and Ie-5, respectively. The number of transformer blocks T in each encoder, contact map-grounded fusion block, and decoder is set to Fine-tuning conducted with a batch size of 64, utilizing the AdamW optimizer [45] with the momentum parameters 31 and 32 initialized to 0.9 and 0.999, respectively. The fine-tuning process configured with a maximum of 200 epochs, and an early stopping strategy is employed with a patience parameter of 20 to prevent overfitting.
B Analysis of Information Gap Between Bimodal Representations.
Simple alignment of multimodal features can result in information loss, which may compromise the performance of downstream tasks. Under conditions of perfect feature alignment, the prediction error using aligned features is at least I, greater than that using the raw inputs. Based on [36), we generalize the theorem to the case of two modalities as Theorem [ This theorem implies that if one modality is more informative than the others (i.e., there exists significant information gap), perfect aligmment may lead to a substantial increase in prediction error. The underlying reason is that perfect alignment constrains the aligned features to preserve only the predictive information shared across all modalities, which may result in the loss of modal-specific information that is potentially critical for achieving accurate predictions. The proof of this theorem is presented follows:
Consider the joint mutual information U(F1, F2;1). Applying the chain rule of mutual information, we obtain:
U(F', UFF'F²;;)) = UUFFFFFF)) U(F; (F) + +t
(10)
(11)
Under the condition of perfect alignment between the two features, U (FF::t = U(F
= 0,
which implies:
U(F F2:t) U (F?):t).
(12)
By applying the data processing inequality [46]:
u(F::t) U(I;t): U(zit),
(13)
we derive UFF1, F2;() f) follows:
F2;") mi)
< m)nn(U(( (>2;
S ()
< (2),))
16

---

According to the variational form of conditional entropy (( iinf ( 22). 47 and the definition of mutual information (XX; H ((X we can conclude the
theorem as follows:
-inf (x inf (cE (F) - "
[[[FFF F²) (1 22)
H(t) -UF') FF (H (211,2200))
((11, UFF", (2;
T.g
Therefore, although perfect alignment of bimodal features achieves consistency, it risks losing critical modal-specific information, potentially leading higher prediction errors. especially when the information gap between modalities is substantial, as seen in the case of Hi-C contact maps and epigenomic tracks. To solve this problem, we introduce cross-modal interaction and cross- modal mapping blocks to capture both modal-invariant and modal-specific features, enabling more comprehensive representation of bimodal data. The effectiveness of this approach validated through extensive ablation studies.
C
Additional Experimental Results.
C.1 Proportion of Validated Loops versus Predicted Loops
We define the quantitative metric 'proportion' as the ratio of experimentally validated loops to computationally predicted loops [44]. Table/8]preeent a comparative analysis of chromatin interaction loops identified through ChIA-PET experiments and those predicted by different approaches. MIX- HIC exceeds rumner-up methods by 28% and 9% proportion on GM12878 and K562 datasets, respectively.
Table Comparison of ChIA-PETTssuppoted loops with those identified by various deep learning methods.
GM12878
K562
Methods
Validuted Loops
Predicted Loops
Proportion
Validated Loops
Predicted Loops
Proportion
HiCExplorer
1358
2688
0.51
765
1450
0.53
ChromoSight
3845
6n44
n.64
993
1628
0.61
Peakachu
1992
2904
0,69
795
1492
0.53
DllopCaller
4301
6878
0.62
1048
2274
0.46
MIX-HIC-Bimaee (Ours)
5179
5893
0.88
1064
1588
0.67
C.2
Example of Whole-Chromosome Chromatin Loop Comparison
Figure 6 illustrates the performance evaluation of chromatin loops predicted by various methods (black points, lower left) against corresponding experimentally validated interactions (red points, upper right). Specifically,,
use the same genome regions, chromosome 13: 20Mb-22Mb from the GM12878 dataset, and identical ground truth data for evaluating all compared methods. Our predictions (the first left panel) demonstrate significantly fewer false positive loops compared to experimentally validated ChIA-PET interactions, as evidenced by the correspondent counts between red and black points. Our method achieves a proportion of 0.57, compared the runner-up" 0.32, highlighting MIX-HIC's superior ability to accurately identify chromatin loops while producing fewer false positives.
C.3 Hyperparameter Analysis
The number of Transformer T blocks in each layer of the encoder, contact map- grounded fusion block. and decoder, along with the predefined feature dimension play a pivotal role in MIX-HIC. We evaluate the impact of this parameter by testing C' = [64, 128, 256) and T = {2. 4, 8} as depicted in Figure and Figure g respectively. The highest performance achieved at = 128 for HI-C contact maps and chromatin loops prediction tasks, while C 256 performs best for CAGE-seg
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MIX-HIC-Bimodal
Peskachu
DLoopCaller
ChromoSight
HiCExplorer
All
Proportion
0.57
0.24
0.32
0.12
0.27
Figure 6: Comparison of predicted chromatin loops (black points, lower left) and corresponding experimentally validated ChIA-PET interactions (red points, upper right) in the chromosome 13 region (20Mb-22Mb) from the GM12878 dataset. Unmatched experimentally validated ChIA-PET interactions are represented by green points the upper right.
expression prediction. Additionally, T' = 2 demonstrates the most robust performance across all three downstream tasks. MIX-HIC exhibits strong resilience to parameter variations, demonstrating our model's 's robustness and parameter efficiency. The performance suggests our architecture reaches "sweet spot" with moderate parameter count and computational efficiency. sufficient to capture the essential biological patterns without incurring the high risk of overfitting during fine-tuning on smaller, task-specific datasets. As our pre-training corpus expands, we anticipate larger architectures will become beneficial. These experimental results substantiate the parameter configurations employed in MIX-HIC.
Hi-C Contact Maps Prediction
Chromatin Loops Prediction
CAGE-seq expression Prediction
A
GM12878 K352
GM12578
K562
GM12576
K382
0.85
0.55
0.85
AUROC
0.00
0.50
00 0.00
0.80
0.85
0.80
0.50
0.80
0.60
128
256
64
120
256
04
128
256
Feature Dimensioo
Festura Dimansion
Festure Dimarsioo
Figure 7: The performance across different predefined feature dimensions C {64, 128, 256} for three downstream tasks in GM12875 and K562 cell lines.
HI-C Contact Maps Prediction
Chromatin Loops Prediction
CAGE-seq expression Prediction
GM12878
KS82
GM12878
KS82
GM12878
K362
0.85
0.95
AUROC
28
0.90
0.50
0.90
0.85
0.85
0.86
0.80
0.80
0.50
2
Layor
Layer
Layer
Figure 8: The performance across different Transformer layers T [{2,488) for three downstream tasks in GM12878 and K562 cell lines.
C.4 Orthogonal Constraint Enhances Feature Diversity
Theorem demonstrates that rigorous alignment can be detrimental to performance. The orthogonal constraint effectively mitigates this issue by promoting diversity between modality-invariin and specifi features, rather than enforcing strict non-overlap. To validate feature diversity,
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compute the inner product between modality-invarian X and modality-specifie X features of
Hi-C contact maps, as well as between their counterparts X and X epigenomic tracks on the datasets of the CAGE-seq expression prediction task. As summarized in Table the inner products under orthogonal constraints are orders of magnitude smaller than those without constraints (e.g., 1e-5 versus 1.077 for Hi-C contact maps on GM12878 dataset). Our analysis of inner product results between these embeddings confirms that the constraint successfully generates near-orthogonal representations.
Table 9: Inner products between modality invariant and modality specifi-cpeeiiceeee features.
With Pre-training
Without Pre-training
Dataset
Hi-C contact maps
Epigenomic tracks
Hi-C contact maps
Epigenomic tracks
GM12878
0.003 + 3e-4
0.002 + 0.001 0.002 + 0.002
1.419 + 0.095
0.164 + 0.059
K562
1e-5 - 4e-4
1.077 = 0.074
0.144 + 0.057
C.5 In Silico Perturbation Validates Biological Grounding of Chromatin Loop Detection
The presence and strength of certain epigenomic signals are highly associated with the formation of For example, CTCF binding sites are typically located within regions of open chromatin, identifiable as peaks in assays such as DNase-seq or ATAC-seq.
chromatin loops 48 49
To validate that MIX-HIC learns the fundamental principle linking epigenomic signals 3D genome architecture, we design an silico perturbation experiment. We hypothesize that the model's loop detection should be governed by the underlying epigenomic signals at the loop anchors. We focus our analysis on 118 high-confidence chromatin loops in the K562 cell line, each characterized by convergent CTCF motifss their anchors (identified via FIMO (50)). We then systematically attenuate the input epigenomic tracks (ATAC-seq and DNase-seq) by down-sampling the signal intensity of peaks within these anchor regions varying ratios. These perturbed epigenomic profiles then fed into the HOX-HIC-Imme InferMaara model (trained on GM12878) to assess the impact on loop detection. The results are shown in Table
Using the unaltered epigenomic data, MIX-HIC successfully recalls all 118 CTCF-mediated loops. As we progressively degrade the epigenomic signals at the loop anchors, the model's recall for these loops decreases. This demonstrates MIX-HIC's predictions are mechanistically grounded in biologically pertinent epigenomic features. This result demonstrates the model's biological interpretability, confirming the capture of a fundamental principle of genome organization.
Table 10: Impact of Epigenomic Signal Attenuation on MIX-HIC Loop Recall.
Varying ratio
0.0
0.5
0.7
0.8
0.9
MIX-HIC-IInfeMaap
100% (118)
98%
(116)
61% (72)
15% (18)
0% (0)
C.6 Robustness Analysis Under Noisy Conditions
The inherent noise and sparsity Hi-C data [2 present a critical challenge for developing robust and generalizable genomic models. To systematically assess the robustness of MIX-HIC, conduct a controlled experiment simulating low-coverage and noisy scenarios. We corrupt Hi-C contact maps by perturbing different ratios of non-zero contacts with sparsity and Gaussian noise. Under these varying noise levels, MIX-HIC and the supervised baseline Peakachu are evaluated on the chromatin loop detection task using the K562 cell line dataset.
As shown in Table [1 the performance of Peakachu degrades sharply as noise increases, falling to near-random performance (0.5091 AUROC) when 70% of the contacts are disturbed. In contrast, MIX-HIC exhibits remarkable robustness, with its performance declining by less than 7% around all noise ratios.
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We attribute this resilience to the power of our pre-training paradigm, which learns the fundamental principles of 3D genome organization from over 1 million samples. This experiment demonstrates that MIX-HIC develops a robust biological representation and is thus particularly suitable for analyzing noisy or low-coverage datasets, We will add this analysis to our manuscript to further strengthen the paper.
Table 11: AUROC comparison of MIX-HIC and Peakachu chromatin loop detection under varying
levels of simulated noise and sparsity.
Varying ratio
0.0
0.5
0.7
0.9
Peakachu MIX-HIC
0,8833
0.7659
0.5091
0.9194
0.8899
0.8754
0.8486
D Baseline Methods and Assets
In this study, the effectiveness of MIX-HIC is evaluated on three downstream tasks, including Hi-C contact map prediction, chromatin loop detection, and CAGE-seq expression prediction. We employ four kinds of methods involving Epiphany [5) C.Origami (6. EPCOT-LSTM [II] and EPCOT- Transformer [ for comparison on the Hi-C contact map prediction task. For chromatin loop detection task, two statistical- biaisseill-bbedd methods (ChromoSight [9] and HiCExplorer (22)). as well as two supervised tearning-basee approaches (Peakachu [8] and DLoopCaller (10)) are utilized for
evaluation. The CAGE-seq expression prediction involves EPI-CNN 12], EPI-Graph [12.. EPCOT-
LSTM I1. and EPCOTT-ransformer to compare with MIX-HIC. More details of these baselines described below.
Epiphany predicts cell-type-specific Hi-C contact maps using bidirectional LSTMs to encode epigenomic tracks.
C.Origami 6 introduces a multimodal framework that predicts chromatin organization from DNA sequence, CTCF binding signals, and epigenomic tracks, enabling the effective identification of regulatory elements.
ChromoSight [9] proposes a computer vision- vision-inspired algorithm for chromatin loop detection that employs expert-defined pattern templates, demonstrating computational efficiency across diverse species without requiring training data.
HiCExplorer [2 identifies significant chromatin interactions by analyzing Hi-C contact matrices, employing binomial distribution modeling distinguish true loops from background noise while controlling for distance-dependent contact probability.
g develops a supervised random forest classification framework that leverages chro- matin interaction labels to predict chromatin loops from Hi-C contact maps, outperforming statisti- enrichment methods identifying short-range interactions.
Peakachu
Xu et al. [10 propose DLoopCaller, a supervised deep learning method that predicts genome-wide chromatin loops by integrating epigenomic tracks with raw Hi-C contact maps.
Karbalayghareh et al. 122 extracts local features from epigenomic tacks using convolutional neural networks (EPI-CNN) and incorporates Hi-C contact maps through graph attention networks (EPI-Graph) to predict CAGE seq expression.
EPCOT [1 former introduces a pre-training and fine-tuning deep learning method that leverages Trans- LSTM (EPCOT-LSTM) architectures to predict Hi-C contact maps and CAGE-seq expression profiles from epigenomic tracks and DNA sequences, achieving generalizable representations across diverse cell types.
The methods Epiphany, C.Origami, ChromoSight, Peakachu, and DLoopCaller are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), while EPI-CNN, EPI-Graph, Frroo--ransformerr, and EPCOT-LSTM, are available under an Attribution-Ne Atrribution-onnommeenn Commomeriiaall 4.0 International License (CC BY-NC 4.0). HiCExplorer is distributed under the GNU General Public License v3.0 (GPL.-3.0). The licenses of other open- tools utilized in this work are summarized in Table 22
20

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Table 12: License of softwares used in this study.
Software
License
URL
Juicer
MIT license
PyBigWig
MIT license
http:///gthhbbccmm/eeelool/////
hicstraw
MIT license
http:s//ggihuu..ccm///ieeeaaa//s
Huggingface
Apache-2.0
http:://uuggiiggacee...
Scikit-Leam
BSD-3-Claus,
attps:/Bcckttllemm.oog/aaaa
Numpy
ASD-3-Clause
https://numpy.cr
Pytorch
BSD-3-Clane 3-Clause
https://pytorch.co attps://matpottlb.org
Matplotlib
Matplotlib License
E
Broader Impacts and Limitations
Broader Impacts. The Iiree-dimensional chromatin architecture fundamentally governs both cellular differentiation and disease progression by mediating genomic interactions. This necessitates the development of Hi-C foundation models to systematically unravel the mechanistic basis of gene regulatory networks in both physiological and disease contexts. MIX-HIC fundamentally advances 3D genomics analysis by addressing two critical limitations of current approaches. First, they are typically designed for single tasks with limited cross-task knowledge transfer capability. Second, they predominantly rely on single-modality data (either Hi-C or epigenomic tracks alone) due to the scarcity of paired multimodal datasets, resulting in incomplete understanding of chromatin organization.
To address these challenges, develop MIX-HIC, the first multimodal foundation model for 3D genomics, with three key innovations: 1) We curate the largest paired dataset (over I million samples) of Hi-C and epigenomic tracks to overcome data scarcity. Our novel cross-modal interaction and mapping blocks simultaneously capture both modality-invariant and modality-specific features, enabling a reliable complement of missing modality features. 3) As foundation model, MIX-HIC enables versatile adaptation to diverse downstream tasks, facilitating knowledge transfer across different 3D genomies tasks where current approaches operate in isolation. In summary, MIX- HIC provides a universal computational platform for systematically deciphering the coordinated mechanisms between chromatin spatial organization and epigenetic regulation, while pioncering new avenues for multimodal data integration in discase mechanism research, precision medicine, and synthetic biology applications.
Limitations. Although MIX-HIC has exhibited promising performance, several areas warrant further improvement. Current methods in the 3D genome field for processing long-range DNA sequences often time-intensive. future work, MIX-HIC could further enhance its capabilities by integrating DNA sequence information through leveraging recent advancements like MambaDNA (20) and HyenaDNA (53) to facilitate feature extraction of genomic sequences. Additionally, while the current version of MIX-HIC serves bulk-seq level foundation model, developing single-cell version through the integration of large-scale multimodal single-cell data would be valuable to effectively address the inherent sparsity and noise in single-cell analyses. Finally, the diversity of cell lines in our pre-training set is an area for future enhancement. Expanding the pre-training corpus by integrating the paired Hi-C and epigenomic datasets available from ENCODE will broaden MIX- HIC's applicability across wider range of biological contexts. Overall, this study presents MIX-HIP HIC. versatile foundation model that integrates 3D genome structures with chromatin accessibility, providing an efficient framework for advancing genomic organization research and related fields.
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Reviews:
[{"review_id": "6tXARWRh9r", "reviewer": "Reviewer_RYdS", "summary": "This paper introduces MIX-HIC, a multimodal pretraining framework designed to learn rich representations of 3D genome structure and epigenomic signals. The method integrates Hi-C data (3D genome conformation) and epigenomic tracks (e.g., DNase, ChIP-seq signals) across hundreds of cell types to train a transformer-based model.", "questions": "We consider the prediction task: Use epigenomic tracks to predict the Hi-C contact map. How many benefits can you get from using contrastive learning? I.e., if you directly train a model (the same architecture as shown in the paper) using epigenomic tracks as inputs to predict the Hi-C contact map, what is the performance compared with your model?", "limitations": "The author should provide more biological background in the paper, as this is a conference primarily focused on machine learning. The reviews are hard to understand the biological part.\n\nSparse Evaluation on Rare Cell Types: Performance in rare or noisy datasets (e.g., low-coverage Hi-C) is not discussed.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": ">Strengths\n1. Innovative Multimodal Integration: Combines epigenomic tracks and Hi-C contact maps, which are typically modeled separately.\n\n2. Strong Pretraining Design: The contrastive learning objective aligns the 1D (track) and 2D (Hi-C) modalities, improving cross-view consistency.\n\n3. Empirical Performance: Demonstrates state-of-the-art results on multiple downstream tasks:\n\n>Weaknesses\n1. Lack of Interpretability: The model is powerful but opaque — biological interpretability of learned embeddings is not deeply explored.\n\n2. Limited Ablation on Modalities: The contribution of each modality (Hi-C vs. epigenomic) is not independently quantified in every downstream task.\n\n3. Sparse Evaluation on Rare Cell Types: Performance in rare or noisy datasets (e.g., low-coverage Hi-C) is not discussed.", "was_revised": true, "final_justification": "Thanks for the response from the authors. All my concerns are solved, and I assign equal weights to my concerns. I appreciate the contribution from the authors, and this paper is very interesting and meaningful. I keep my score as 4 and support for the acceptance of this paper.", "rebuttal": "We thank the reviewer for the constructive comments. We provide the following responses regarding the concerns.\n> #### **Q1. Lack of Interpretability.**\n\nWe thank the reviewer for the insightful feedback. The presence and strength of certain epigenomic signals are highly associated with the formation of chromatin loops [1, 2]. For example, CTCF binding sites are typically located within regions of open chromatin, identifiable as peaks in assays such as DNase-seq or ATAC-seq.\n\nTo validate that MIX-HIC learns the fundamental principle linking epigenomic signals to 3D genome architecture, we design an in *silico* perturbation experiment. We hypothesize that the model's loop predictions should be governed by the underlying epigenomic signals at the loop anchors. We focus our analysis on 118 high-confidence chromatin loops in the K562 cell line, each characterized by convergent CTCF motifs at their anchors (identified via FIMO [3]). We then systematically attenuate the input epigenomic tracks (ATAC-seq and DNase-seq) by down-sampling the signal intensity of peaks within these anchor regions at varying ratios. These perturbed epigenomic profiles are then fed into the MIX-HIC-InferMap model (trained on GM12878) to assess the impact on loop prediction. The results are shown below.\n\n| Varying ratio     | 0.0        | 0.5       | 0.7      | 0.8      | 0.9    |\n| ---------------- | ---------- | --------- | -------- | -------- | ------ |\n| MIX-HIC-InferMap | 100% (118) | 98% (116) | 61% (72) | 15% (18) | 0% (0) |\n\nUsing the unaltered epigenomic data, MIX-HIC successfully recalls all 118 CTCF-mediated loops. As we progressively degrade the epigenomic signals at the loop anchors, the model’s recall for these loops decreases. This demonstrates MIX-HIC's predictions are mechanistically grounded in biologically pertinent epigenomic features. This result demonstrates the model's biological interpretability, confirming the capture of a fundamental principle of genome organization.\n\n[1] Zhao et al. \"Chromatin loops associated with active genes and heterochromatin shape rice genome architecture for transcriptional regulation.\" *Nature Communications*, 2019.\n\n[2] Sahin et al. \"HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP.\" *Nature Communications,* 2021.\n\n[3] Grant et al. \"FIMO: scanning for occurrences of a given motif.\" *Bioinformatics*, 2011.\n\n> #### **Q2. Limited Ablation on Modalities.**\n\nWe thank the reviewer for this important question. Evaluating the contribution of each modality is fundamental to demonstrating the value of our multimodal approach. We have performed direct single-modality ablations for every task where such a comparison is both methodologically sound and scientifically informative.\n\nFor the chromatin loop detection task, a direct comparison is highly informative. As shown in Table 6, we explicitly quantify the performance of epigenomics-only, Hi-C-only, and MIX-HIC-Bimodal. On the GM12878 dataset, for instance, MIX-HIC-Bimodal achieves the best performance with an AUROC of 0.9209, surpassing the epigenomics-only and Hi-C-only variants by approximately 9% and 2%, respectively, demonstrating a clear synergistic gain.\n\n| Epi. | Hi-C | Pre-trained | GM12878    | K562       |\n| ---- | ---- | ----------- | ---------- | ---------- |\n| ✔    | -    | -           | 0.8236     | 0.8054     |\n| -    | ✔    | -           | 0.9065     | 0.9072     |\n| ✔    | ✔    | ✔           | **0.9209** | **0.9194** |\n\nHowever, such a single-modality ablation is conceptually inappropriate for the other two tasks. For the Hi-C contact map prediction task, predicting the Hi-C contact map from itself would be a trivial identity mapping, not a meaningful ablation. For the CAGE-seq expression prediction task, a Hi-C-only baseline may not be scientifically meaningful. CAGE-seq expression is a direct functional readout driven by local epigenomic features (e.g., promoter accessibility), which is why state-of-the-art methods (e.g., EPCOT [1], GraphReg \\[2]) only focus on these 1D signals. In contrast, Hi-C provides the long-range genomic 'wiring diagram' but lacks the direct signal for transcriptional activity level [3]. Using the Hi-C contact map in isolation is therefore an indirect and weak predictor, making it an inappropriate baseline. Therefore, MIX-HIC respects this biological framing, using the Hi-C contact map to provide structural context to the primary epigenomic predictors, rather than as a standalone source [4].\n\nIn summary, we provide direct modality ablation only where it is informative and will add this rationale to the manuscript.\n\n[1] Zhang et al. \"A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.\" *Nucleic Acids Research*, 2023.\n\n[2] Karbalayghareh et al. \"Chromatin interaction–aware gene regulatory modeling with graph attention networks.\" *Genome Research*, 2022.\n\n[3] Nora et al. \"Spatial partitioning of the regulatory landscape of the X-inactivation centre.\" *Nature*, 2012.\n\n[4] Zhang et al. \"Computational methods for analysing multiscale 3D genome organization.\" *Nature Reviews Genetics*, 2024.\n\n> #### **Q3. Performance in rare or noisy datasets is not discussed.**\n\nWe thank the reviewer for this perceptive question. The inherent noise and sparsity of Hi-C data [1, 2] present a critical challenge for developing robust and generalizable genomic models. To systematically assess the robustness of MIX-HIC, we conduct a controlled experiment simulating low-coverage and noisy scenarios. We corrupt Hi-C contact maps by perturbing different ratios of non-zero contacts with sparsity and Gaussian noise. Under these varying noise levels, MIX-HIC and the supervised baseline Peakachu are evaluated on the chromatin loop detection task using the K562 cell line dataset.\n\n| Varying ratio | 0.0    | 0.5    | 0.7    | 0.9    |\n| ------------ | ------ | ------ | ------ | ------ |\n| Peakachu     | 0.8833 | 0.7659 | 0.5091 | -      |\n| MIX-HIC      | 0.9194 | 0.8899 | 0.8754 | 0.8486 |\n\nAs shown in the table, the performance of Peakachu degrades sharply as noise increases, falling to near-random performance (0.5091 AUC) when 70% of the contacts are disturbed. In contrast, MIX-HIC exhibits remarkable robustness, with its performance declining by less than 7% around all noise ratios.\n\nWe attribute this resilience to the power of our pre-training paradigm, which learns the fundamental principles of 3D genome organization from over 1 million samples. This experiment demonstrates that MIX-HIC develops a robust biological representation and is thus particularly suitable for analyzing noisy or low-coverage datasets. We will add this analysis to our manuscript to further strengthen the paper.\n\n[1] Yardımcı et al. \"Measuring the reproducibility and quality of Hi-C data.\" *Genome Biology*, 2019.\n\n[2] Spill et al. \"Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution.\" *Nature Communications*, 2019.\n\n> #### **Q4. Quantifying the benefit of pre-training vs. supervised baseline (i.e., epigenomic tracks).**\n\nThank you for the question. The reviewer asks for a direct comparison against a model with an identical architecture but trained from scratch on the downstream task, which is precisely the ablation we perform to isolate the benefit of our pre-training. Comparison results are shown in Table 6.\n\n| Epi. | Hi-C | Pre-trained | GM12878    | K562       |\n| ---- | ---- | ----------- | ---------- | ---------- |\n| ✔    | -    | -           | 0.8481     | 0.7709     |\n| ✔    | ○    | ✔           | **0.8724** | **0.8001** |\n\nThe first row is a model with the exact same architecture but with weights initialized randomly and trained directly (end-to-end) on the downstream prediction task. The second row is the MIX-HIC-InferMap model that benefits from our full pre-training pipeline. Experimental results highlight the efficacy of our pre-training, increasing the R² score by approximately 3% on GM12878 and 4% on K562. In the revised manuscript, we will add related descriptions to highlight the gain from pre-training against an end-to-end supervised baseline.\n\n> #### **Q5. Provide a more biological background.**\n\nWe sincerely thank the reviewer for this constructive suggestion. Following this advice, we have revised the manuscript to integrate more explicit biological context. Here, we offer some examples to demonstrate how we have integrated this valuable feedback throughout the manuscript:\n\n1. Clarifying the function of chromatin loops (line 17): We define loops by their function: \"*Key topological features of the 3D genome, such as chromatin loops that bring distant regulatory elements into close physical proximity with their target genes [1], are essential for cell-type-specific transcriptional regulation.*\"\n\n2. Defining key biological terms and assays (line 116): We add concise definitions for key inputs and labels: \".*..while the epigenomic tracks (ATAC-seq and DNase-seq, which measure how 'open' or accessible DNA is for transcription), CAGE-seq expression data (which directly quantifies gene activity levels), and CTCF ChIA-PET chromatin loops (which identify high-confidence interactions mediated by the key architectural protein CTCF) are downloaded from the ENCODE Portal...\"*\n\n3. Clarifying the input/output setup for the Hi-C contact map prediction task (line 398): To avoid confusion about the task setup, we explicitly state: \"*For the Hi-C contact map prediction task, the model is trained to predict the contact map using only 1D epigenomic tracks as input. The Hi-C contact map itself is the prediction target (output), making its use as an input feature methodologically invalid.*\"\n\nWe are confident these revisions make the biological motivation clear and allow the conference reader to better appreciate the context and significance of our technical contributions."}, {"review_id": "2dRkkG66J3", "reviewer": "Reviewer_B3i8", "summary": "This paper proposes a deep neural network (DNN) architecture called **MIX-HIC** for DNA analysis. The model takes as input Hi-C data alongside several genomic assays—including ATAC-seq, DNase-seq, CAGE-seq expression data, and CTCF ChIA-PET. It is pretrained with a representation-learning objective and then fine-tuned for downstream tasks. The contributions are as follows:\n\n- **A model for learning joint representations** of Hi-C and genomic assay tracks.  \n- **A large-scale paired dataset** comprising Hi-C maps and common genomic assay tracks.  \n- **Demonstrated applications** of the model to genomic track prediction.", "questions": "- **What is the total parameter count for the MIX-HIC model?**  \n  It would be helpful if the authors showed how model size affects the pre-training loss. According to the ablation study in Figure 8, performance appears insensitive to model size, which is counter-intuitive. If that observation is accurate, why does model size have so little impact on downstream performance?\n\n- **Why was MSE loss used for CAGE-track prediction?**  \n  The common practice is to employ the Poisson negative log-likelihood loss, as in [1] and [2].\n\n- **Could you report Enformer’s zero-shot performance on the CAGE-track prediction task?**  \n  Including Enformer [1] as a baseline would clarify how MIX-HIC compares with sequence-based models.\n\n\n[1] Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J.R., Grabska-Barwinska, A., Taylor, K.R., Assael, Y., Jumper, J., Kohli, P. and Kelley, D.R., 2021. Effective gene expression prediction from sequence by integrating long-range interactions. Nature methods, 18(10), pp.1196-1203.]\n\n[2] Kelley, D. R. Cross-species regulatory sequence activity prediction. PLoS Comput. Biol. 16, e1008050 (2020).", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n- The paper is well-motivated; Hi-C is an important data type that should be integrated into DNA-sequence modelling.  \n- The model architecture, training-loss design, and ablation study are comprehensive.  \n- The experiments include both conventional and deep-learning baselines to validate performance.  \n\n**Weaknesses**\n\n- The absence of a DNA-sequence modality restricts the model’s use cases, especially because many available tasks use DNA sequence as the sole input.  \n- The terms “3D genome” and “Hi-C” are not interchangeable. While Hi-C provides a good indicator of the genome’s 3D conformation, it does not fully capture it. Previous work predicts 3D genome structure from both Hi-C and DNA sequence. The DNA sequence itself determines physical properties—e.g., bond rigidity between adjacent nucleotides and the ease with which DNA bends—that may be important.  \n- See the *Questions* section for detailed inquiries about the experiments.", "was_revised": true, "final_justification": "I will maintain my original score. The authors’ rebuttal provides additional clarification on the model training details. Although the responses to Q4 and Q5 were somewhat unclear, the overall quality of the paper remains fair.", "rebuttal": "Thank you for your constructive feedback! We treasure the opportunity to address your concerns.\n\n> #### **Q1. The absence of a DNA-sequence modality restricts the model’s use cases.**\n\nThank you for this forward-looking suggestion. We agree that incorporating DNA sequence is a critical next step to create a truly comprehensive 3D genome model. We'd like to clarify the strategic rationale for our current focus and our clear roadmap for future integration.\n\nOur primary goal is to pioneer the first multimodal foundation model for 3D genome, integrating 3D architecture (Hi-C contact maps) and cell-type-specific regulatory state (epigenomics). As established by prior works [1, 2], the interplay between these two modalities provides potent signals for predicting dynamic genomic functions like gene expression and chromatin looping across different cell types.\n\nHowever, modeling long-range DNA sequences encounters two critical challenges: cell-type invariance and computational inefficiency. Since the DNA sequence is static, models trained on DNA sequences would struggle with generalization across different cell types. Moreover, processing megabase-scale DNA inputs demands significant computational resources, creating a significant bottleneck for large-scale pre-training and downstream applications. Current MIX-HIC circumvents these challenges while establishing a strong baseline for multimodal 3D genome modeling. We view this as a pragmatic and foundational first step.\n\nRecent breakthroughs in long-range DNA modeling (e.g., HyenaDNA [3] and AlphaGenome [4]) make the direct integration of sequence data into our framework more feasible and promising. As we mentioned in the \"Broader Impacts and Limitations\" section, we plan to further incorporate DNA sequence into MIX-HIC for capturing the full hierarchy of genome regulation. The related descriptions will be added in the camera-ready manuscript.\n\n[1] Tan et al. \"Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening.\" *Nature Biotechnology*, 2023.\n\n[2] Yang et al. \"Epiphany: predicting Hi-C contact maps from 1D epigenomic signals.\" *Genome Biology*, 2023.\n\n[3] Nguyen et al. \"HyenaDNA: Long-range genomic sequence modeling at single nucleotide resolution.\" *Advances in Neural Information Processing Systems,* 2023.\n\n[4] Avsec et al. \"AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model.\" *bioRxiv*, 2025.\n\n> #### **Q2. The terms “3D genome” and “Hi-C” are not interchangeable. Previous work predicts 3D genome structure from both Hi-C and DNA sequence.&#x20;**\n\nThank you for this crucial feedback. We completely agree that the terms \"3D genome\" and \"Hi-C\" are not interchangeable. As stated in our introduction, our intent is to define the Hi-C technique as a method to \"quantify high-resolution 3D chromatin interactions,\" thus positioning it as a powerful measurement of 3D genome organization, not the entity itself.\n\nTo address your feedback, we have performed a thorough review of the manuscript to eliminate any potential ambiguity. For instance, the phrase \"...integrates both 3D genome structure and epigenomic tracks\" has been revised to \"...integrates both Hi-C contact maps and epigenomic tracks.\"\n\nWe want to assure the reviewer that this reflects a potential for misinterpretation in our phrasing, not a conceptual misunderstanding in our methodology. The model's architecture and experimental design are soundly based on the distinct roles of Hi-C data and epigenomic tracks, with the ultimate goal of integrating these views to form a comprehensive representation of 3D genome organization. We also concur on the importance of the DNA sequence, and as discussed in our response to **Q1**, its integration is a central pillar of our future work.\n\n> #### **Q3. Total parameter count for the MIX-HIC model. How model size affects pre-training loss. Why does model size have so little impact on downstream performance?**\n\nThank you for these insightful questions. Our primary configuration (128-feature dimension, 2-Transformer layers) has approximately 74 million parameters. The pre-training loss across different model sizes is summarized below.\n\n| Layers | Epoch 1 | Epoch 20 | Epoch 50 | Epoch 100 |\n| ------ | ------- | -------- | -------- | --------- |\n| 2      | 3.0648  | 0.0898   | 0.0416   | 0.0224    |\n| 4      | 3.0519  | 0.0867   | 0.0383   | 0.0171    |\n| 8      | 3.0227  | 0.0838   | 0.0329   | 0.0108    |\n\nWhile larger models achieve lower pre-training loss, this does not translate to better downstream performance (as demonstrated in Figure 8), aligning with previous observations [1]. We argue this is a key strength, demonstrating our model's robustness and parameter efficiency. The performance suggests our architecture reaches a \"sweet spot\" [2] with a moderate parameter count and computational efficiency, sufficient to capture the essential biological patterns without incurring the high risk of overfitting during fine-tuning on smaller, task-specific datasets.\n\nAs our pre-training corpus expands, we anticipate larger architectures will become beneficial. We thank the reviewer for this suggestion and will add this analysis to the final manuscript.\n\n[1] Liu et al. \"Same pre-training loss, better downstream: Implicit bias matters for language models.\" *International Conference on Machine Learning*, 2023.\n\n[2] Nakkiran et al. \"Deep double descent: Where bigger models and more data hurt.\" *Journal of Statistical Mechanics: Theory and Experiment*, 2021.\n\n> #### **Q4. Why was MSE loss used for CAGE-track prediction, instead of Poisson negative log-likelihood loss.**\n\nThank you for your question regarding our choice of loss function. The Poisson negative log-likelihood loss is suitable for modeling raw genomic counts. However, such data are characterized by a high dynamic range and significant overdispersion [1, 2], which may create challenges during model training and may lead to a model that is overly sensitive to extreme outliers.\n\nTo address this, we apply both RPGC normalization and a log transformation to the CAGE-seq expression data following previous works [3, 4]. These transformations are critical for stabilizing the variance across the spectrum of expression values and compressing the highly skewed distribution of the raw counts data. After transformation, the data distribution more closely approximates a Gaussian distribution instead of a Poisson distribution. Consequently, the MSE loss becomes a more appropriate and robust choice for these continuous values [3, 4].\n\nWe will incorporate related clarification of this rationale into the Methods section of the final manuscript.\n\n[1] Robinson et al. \"edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.\" *Bioinformatics*, 2010.\n\n[2] Love et al. \"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.\" *Genome Biology*, 2014.\n\n[3] Zhang et al. \"A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.\" *Nucleic Acids Research*, 2023.\n\n[4] Karbalayghareh et al. \"Chromatin interaction–aware gene regulatory modeling with graph attention networks.\" *Genome Research*, 2022.\n\n> #### **Q5.  Compared with sequence-based models on the CAGE-track prediction task like performing Enformer’s zero-shot.**\n\nWe appreciate the reviewer's constructive comments. Comparing MIX-HIC to a landmark sequence-based model like Enformer would indeed provide valuable context. However, a direct, quantitative benchmark is challenging due to a fundamental mismatch in the task formulation. Enformer is designed to predict raw CAGE-seq expression counts, while MIX-HIC predicts normalized and log-transformed expression values for variance stabilization. Directly comparing these results would not be a fair or meaningful evaluation. While retraining Enformer on our specific task formulation would be computationally prohibitive for this rebuttal period, we will provide such a comparison in our public code repository.\n\nDespite the absence of a direct Enformer benchmark, our experiments have offered strong insights in comparison with sequence-based models. Our CAGE-seq prediction benchmark (Table 4) includes a comprehensive comparison against EPCOT, another powerful DNA sequence-based model. \n\n| Methods | GM12878 | K562 |\n|---------|---------|--------|\n| EPCOT-LSTM             | 0.4723  | 0.8704 |\n| EPCOT-Transformer      | 0.8578  | 0.8230 |\n| MIX-HIC-Bimodal (Ours) | 0.8833  | 0.9077 |\n\nThe results demonstrate that MIX-HIC outperforms the best EPCOT variant, with gains of 3.0% on GM12878 and 4.3% on K562. Crucially, fine-tuning MIX-HIC for this task takes merely 1-2 hours on a single GPU, whereas fine-tuning the sequence-based EPCOT model requires over a day. These results demonstrate that MIX-HIC achieves robust performance with significantly greater computational efficiency. We will add a detailed discussion of Enformer and efficiency comparison results to the manuscript to further highlight the practical advantage of MIX-HIC."}, {"review_id": "yB8VcwKI8d", "reviewer": "Reviewer_B97T", "summary": "This work proposes to pretrain models for better 3D genomics representation - jointly training models to integrates genome structure information (HiC) and epigenomic tracks. They curate a pretraining data of size 1 million (after cleaning). After training, the model performed the best across tasks such as 3D chromatin organization prediction, loop detection, and subsequence expression prediction.", "questions": "1. Details and citation in Section 3. As the reviewer is not familiar with proprocessing the Hi-C contact maps and the epigenomic tracks, can the authors add citation regarding preprocessing? Also, here are a few questions\n- Is log-transformation and 50 x 50 matrices standard? Would a 5kb resolution be too rough?\n- Why filtering out 10% non-zero Hi-C interactions? How many data is filtered out during this process?\n\n2. Error analysis:\n- What are the main obstacles that stops the MiX-HIC-Bimodal to achieve even higher performance? Have the authors consider performing some error analysis or stratified analysis to dive further into the how and why the proposed model is better than others?\n\n3. Open source\n- Do the authors plan to open-source pre-training data?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-written and the idea presented is very clear.\n2. Bringing forward a dataset of this magnitude is important for this AI4Science domain.\n3. The evaluation is pretty comprehensive and the baselines are strong enough to robustly support the superiority of the proposed model.", "was_revised": true, "final_justification": "The authors answered my questions and resolved my concerns. I recommend an acceptation of this work.", "rebuttal": "We thank the reviewer for all the comments. We believe we have addressed all the concerns and are happy to follow up in the discussion phase.\n\n> #### **Q1. Add citation regarding preprocessing? Is log-transformation and 50 x 50 matrices standard? Would a 5kb resolution be too rough? Why filtering out 10% non-zero Hi-C interactions? How many data is filtered out during this process?**\n\nThank you for these detailed questions. We will revise Section 3 to include more detailed justifications and citations for our data processing pipeline. Our parameter choices and preprocessing steps follow common practices in the 3D genomics field.\n\nFirst, due to the high cost of deep sequencing required for Hi-C experiments [1], publicly available datasets are typically provided at resolutions such as 5 kb, 10 kb, or coarser. A 5 kb resolution is a fine-grained and effective choice for deep learning models [2, 3]. The 50 x 50 matrix size is a direct result of our choice of a 250 kb genomic window (50 bins \\* 5 kb/bin = 250 kb). We chose this window size because it captures the typical length scale of key regulatory structures like chromatin loops, ensuring that most functional units are fully contained within the inputs [4, 5].\n\nSecond, raw Hi-C contact values are count data with a highly skewed distribution and a large dynamic range. Applying variance-stabilizing KR normalization [6] and a log-transformation is a critical step to make the data more stable for modeling with standard deep learning architectures [4, 6, 7].\n\nFinally, the zero Hi-C contacts filtering is a standard quality control step to remove uninformative regions [5, 7]. Hi-C contact maps are inherently sparse, particularly for long-range interactions far from the diagonal. Including these extremely sparse, low-signal windows would introduce noise and degrade model training. As shown in Table 1, this filter removed approximately 30% of the raw windows. Crucially, this process retains a massive, high-quality dataset of over 1.2 million sample pairs, which is more than sufficient for robust pre-training.\n\nWe will integrate these justifications and the relevant citations into the manuscript to make our pipeline clearer and reproducible.\n\n[1] Chang, Lei, et al. \"Droplet Hi-C enables scalable, single-cell profiling of chromatin architecture in heterogeneous tissues.\" *Nature Biotechnology* (2024): 1-14.\n\n[2] Zhang et al. \"A generalizable framework to comprehensively predict epigenome, chromatin organization, and transcriptome.\" *Nucleic Acids Research*, 2023.\n\n[3] Karbalayghareh et al. \"Chromatin interaction–aware gene regulatory modeling with graph attention networks.\" *Genome Research*, 2022.\n\n[4] Yang et al. \"Epiphany: predicting Hi-C contact maps from 1D epigenomic signals.\" *Genome Biology*, 2023.\n\n[5] Salameh et al. \"A supervised learning framework for chromatin loop detection in genome-wide contact maps.\" *Nature Communications,* 2020.\n\n[6] Kaul et al. \"Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2.\" *Nature Protocols*, 2020.\n\n[7] Wang et al. \"DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.\" *PLoS Computational Biology*, 2022.\n\n> #### **Q2. Performance Bottlenecks and Error Analysis.**\n\nThank you for this valuable question. We hypothesize that the primary obstacle preventing MIX-HIC from achieving even higher performance is not in the model architecture but the inherent noise and sparsity of the input Hi-C data. Hi-C experiments are known to be affected by factors like sequencing depth and biases, which can obscure true biological signals [1, 2].\n\nThus, we perform an error analysis by simulating low-coverage sequencing scenarios. Specifically, we corrupt Hi-C contact maps by perturbing different ratios of non-zero contacts with sparsity and Gaussian noise. We then evaluated both our pre-trained MIX-HIC and the supervised baseline, Peakachu, on the chromatin loop detection task.\n\n| Varying ratio | 0.0    | 0.5    | 0.7    | 0.9    |\n| ------------ | ------ | ------ | ------ | ------ |\n| Peakachu     | 0.8833 | 0.7659 | 0.5091 | -      |\n| MIX-HIC      | 0.9194 | 0.8899 | 0.8754 | 0.8486 |\n\nAs shown in the table, the performance of Peakachu degrades sharply as data quality degrades, falling to near-random performance (0.5091 AUC) when 70% of the contacts are disturbed. In contrast, MIX-HIC exhibits remarkable robustness, with its performance declining by less than 7% around all ratios. We attribute this resilience to the power of our large-scale pre-training strategy, which learns the fundamental principles of 3D genome organization from over a million examples. This experiment demonstrates data quality as a key performance bottleneck and provides a clear explanation of the benefits of pre-training.\n\n\\[1] Yardımcı et al. \"Measuring the reproducibility and quality of Hi-C data.\" *Genome Biology*, 2019.\n\n\\[2] Spill et al. \"Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution.\" *Nature Communications, 2019.\n\n> #### **Q3. Do the authors plan to open-source pre-training data?**\n\nThank you for your interest in our work! We are committed to releasing the complete pre-training dataset and the source code for MIX-HIC with detailed documentation upon publication. We believe that providing this large-scale dataset alongside our open-source model will create a valuable foundational resource for the community, ultimately accelerating progress in 3D genomics research."}, {"review_id": "ffY9pO9Mkm", "reviewer": "Reviewer_T1qU", "summary": "This paper proposes a multi-modal foundation model that takes as input Hi-C data and epigenomic data. The model itself includes some interesting techniques to capture cross-modal information while preserving both shared and distinct information from the two modalities.  After pre-training, the model is fine-tuned for several downstream tasks, where it outperforms state-of-the-art methods.", "questions": "The data set is described as if it's very large, but it's really not.  There are only six cell lines being used here, even though there are many more available paired datasets in ENCODE and 4DN.  Why was such a small number of cell lines used?\n\nThe text largely discussed the model inputs in terms of \"epigenomic tracks.\"  Only with some effort did I figure out that the inputs are DNAse-seq and ATAC-seq.  This seems like a weird choice, because these two are largely redundant with one another.  Why not use a histone modification instead?  If you stick with DNAse and ATAC-seq, then you should just refer to this as \"chromatin accessibility\" rather than the more general term \"epigenomic.\"\n\nThe paper claims (line 229) that MIX-HIC can infer a missing modality based on one modality.  I would like to see empirical evidence that this inference is accurate, both from Hi-C to chromatin accessibility and vice versa.\n\nI was pretty surprised by the shape of the learning curves in Figure 4.  I would expect that if you eliminate 99.99% of the training data the performance should drop precipitously for most methods.  I would also expect the gap between a pre-trained model and a non-pre-trained model to get much larger when the training set is tiny.  Why don't we see these trends?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "One fairly significant omission is any reference to or comparison to the existing Hi-C foundation model, HiCFoundation (Wang et al. Biorxiv 2024).  The model proposed here is applicable to more tasks, since it takes in both Hi-C data and epigenomics, whereas HiCFoundation operates only on Hi-C data.  But for loop calling, both models could be compared head-to-head.  And certainly in the related work section, this existing Hi-C foundation model should be discussed.\n\nOne nice feature is that you have done train/test splitting at the level of both chromosomes and cell lines.  On the other hand, I think you should only report the cross-cell line + cross-chromosome results.  I.e., the experiments in Section 5.1 all should have been done in the test set cell lines and the test set chromosomes.  Later sections (e.g., results in Table 5) should also use this setting.  The cross-chromosomal prediction task is not really practically interesting.\n\nYou have also done a good job of identifying state-of-the-art methods to compare against (with the obvious exception of the other Hi-C foundation model mentioned above).\n\nI also like the model design, and in particular the idea of maintaining cell type-specific and cell type-agnostic latent representations of each of the inputs.\n\nThe sentence starting in line 398 does not make sense to me: \"Notably, the Hi-C contact map prediction task inherently predicts contact maps, thus lacking some experiments with Hi-C input.\"  Similarly, this sentence is hard to parse: \"Further constraint for orthogonal constaint [sic] is provided in Appendix C.4.\"  What is a constraint for a constraint?", "was_revised": true, "final_justification": "The authors did a good job of responding to my questions. I still think this is a good candidate for acceptance.", "rebuttal": "We thank the reviewer for a thorough and rigorous examination of our paper. We aim to address the questions and provide clarifications to further improve the work.\n\n> #### **Q1. HiCFoundation comparison and discussion.&#x20;**\n\nThank you for highlighting this relevant work. The omission is due to its concurrent publication as a preprint. HiCFoundation excels at learning deep structural patterns intrinsic to the Hi-C contact matrix. However, it can only infer the regulatory state that drives loop formation from these structural patterns. In contrast, MIX-HIC is designed to learn both structural patterns from Hi-C contact maps and the causal regulatory signals from chromatin accessibility. We benchmark MIX-HIC with HiCFoundation on loop detection in K562 cells.\n\n| Methods       | F1     | AUROC  |\n| ------------- | ------ | ------ |\n| HiCFoundation | 0.8064 | 0.8978 |\n| MIX-HIC       | 0.8267 | 0.9194 |\n\nThe results confirm that MIX-HIC outperforms HiCFoundation by approximately 2%, validating the benefit of our multimodal design. By integrating chromatin accessibility, MIX-HIC is inherently more versatile and applicable to a broader range of tasks. We will incorporate these results and a discussion of HiCFoundation into the manuscript.\n\n> #### **Q2. I think you should only report the cross-cell line + cross-chromosome results in Section 5.1 and later sections.**\n\nThank you for this excellent suggestion. We agree that the combined cross-cell line and cross-chromosome setting is the most rigorous test of generalization. With this in mind, our evaluation is designed in two complementary stages. The cross-chromosome setting (Section 5.1) serves as a foundational benchmark [1, 2], essential for validating that the model has learned the universal principles of chromatin folding rather than memorizing chromosome-specific features.\n\nCrucially, we already performed the most rigorous test: the experiment in Section 5.3 (Figure 5) is precisely the combined cross-cell-line and cross-chromosome evaluation, though we acknowledge our original description may not have made this sufficiently explicit. The results demonstrate that MIX-HIC consistently outperforms other baseline methods in this rigorous setting, providing compelling evidence of MIX-HIC's ability to generalize across entirely unseen cellular and chromosomal contexts simultaneously.\n\nWhile re-running every experiment from scratch under this combined setting is computationally prohibitive for the rebuttal period, the definitive success of MIX-HIC on this most challenging test gives us high confidence in its robust performance across all tasks. We will revise the manuscript to explicitly label the Section 5.3 results as the combined cross-cell-line and cross-chromosome benchmark and to clarify our overall evaluation strategy. Furthermore, we will provide comprehensive results for all tasks under this setting in our public code repository upon publication.\n\n[1] Yang et al. \"Epiphany: predicting Hi-C contact maps from 1D epigenomic signals.\" *Genome Biology*, 2023.\n\n[2] Salameh et al. \"A supervised learning framework for chromatin loop detection in genome-wide contact maps.\" *Nature Communications,* 2020.\n\n> #### **Q3. Two confusing sentences (line 398 and line 389).**\n\nThank you for pointing these out. We apologize for the confusion and have revised them for clarity. We have revised both sentences. The first sentence has been revised as \"*For the Hi-C contact map prediction task, the model is trained to predict the contact map using only 1D chromatin accessibility as input. The Hi-C contact map itself is the prediction target (output), making its use as an input feature methodologically invalid.*\" The second sentence contains a typo and has been corrected to \"*Further details for the orthogonal constraint are provided in Appendix C.4.*\"\n\n> #### **Q4. Why was such a small number of cell lines used?**\n\nThank you for the comment. The number of cell lines is limited because our dataset required pairing Hi-C data from 4DN with corresponding chromatin accessibility from ENCODE, and all cell lines met this criterion have been included in our study. While the number of cell lines is currently limited, our work pioneers the first multimodal 3D genome foundation model. Generating a large-scale, high-quality paired dataset requires meticulous effort, and we have successfully curated over 1.27 million rigorously validated sample pairs. This resource provides the necessary scale for robust pre-training and a valuable foundation for future research in 3D genome analysis.\n\nDespite the limited number of source cell lines, MIX-HIC demonstrates strong cross-cell line + cross chromosome generalization, as we detailed in our response to **Q2**. This robust performance in the most rigorous evaluation setting indicates that our pre-training on this large sample set successfully captured fundamental and generalizable biological representations.\n\nWe agree that increasing cell line diversity is a crucial next step to enhance the model's generalization across a wider range of biological contexts. Expanding this dataset is a key priority for our ongoing work, and we plan to release larger and more diverse datasets to the community in the future.\n\n> #### **Q5. Reasons for choosing DNAse-seq and ATAC-seq as inputs. Why not use a histone modification instead? If you stick with DNAse and ATAC-seq, then you should just refer to this as \"chromatin accessibility\"**\n\nThanks for this excellent feedback. Regarding the choice of DNase-seq and ATAC-seq, while both assays probe chromatin accessibility, their distinct enzymatic biases can provide complementary, rather than purely redundant, signals, creating a more robust combined representation. In addition, we select these two tracks over histone modification due to data availability. Our rigorous cross-database pairing process requires finding high-quality assays that are consistently available across our selected cell lines, and ATAC-seq and DNAse-seq best fulfill this criterion. Moreover, MIX-HIC is inherently flexible and designed to be agnostic to specific 1D genomic track inputs. Integrating key histone modifications like H3K27ac is an important direction for future work to build a more comprehensive model. Finally, we agree that \"chromatin accessibility tracks\" is a more precise term, and we will revise the manuscript accordingly.\n\n> #### **Q6. Missing modality inference from Hi-C to chromatin accessibility and vice versa.**\n\nWe thank the reviewer for this insightful question. We perform an ablation experiment to assess the cross-modal inference ability of MIX-HIC. The results on the loop detection task are shown below.\n\n| Epi | Hi-C | Pre-trained | GM12878 | K562   |\n| --- | ---- | ----------- | ------- | ------ |\n| ✔   | -    | -           | 0.8236  | 0.8054 |\n| ✔   | ○    | ✔           | 0.8494  | 0.8226 |\n| -   | ✔    | -           | 0.9065  | 0.9072 |\n| ○   | ✔    | ✔           | 0.9135  | 0.9159 |\n| ✔   | ✔    | ✔           | 0.9209  | 0.9194 |\n\nThe results demonstrate that augmenting a single modality with its inferred counterpart consistently boosts performance, narrowing the gap to the full bimodal model. This ability stems from the shared latent space learned during large-scale pre-training. We will incorporate the related results into the manuscript.\n\n> #### **Q7. Performance should drop for most methods and the gap between a pre-trained model and a non-pre-trained model should get much larger in low-data regimes.**\n\nThank you for this sharp observation. The performance of most methods remains robust, primarily stemming from the tractable nature of the task and the employed architectures. Powerful and repetitive biological signatures of chromatin loops make them relatively easy for most models to detect. However, architectural differences explain the performance variations. Peakachu, a Random Forest model, is inherently stable, as it relies on engineered features that are less sensitive to data volume. In contrast, DLoopCaller's CNN architecture is more data-hungry and shows a steeper decline. Our MIX-HIC architecture, even without pre-training, proves more data-efficient: the self-attention mechanism is better suited for capturing the global and long-range dependencies in contact maps than local CNNs, and its bimodal input provides complementary information, enhancing robustness even in low-data regimes.\n\nThe reason why the gap between pre-trained and non-pre-trained models does not appear to widen dramatically may be largely attributed to our robust backbone architecture and the limited variety of cell lines available for pre-training. Our backbone architecture has proven notably effective, enabling the non-pre-trained model to already achieve strong performance—this in turn helps narrow the potential gap. As we noted in our response to **Q4**, while the dataset boasts a large sample size, the limited number of cell lines available under our rigorous pairing process may, to some extent, constrain the performance gains from pre-training.\n\nCrucially, pre-training provides a dramatic boost in model stability. For instance, in the 0.01% data experiment on GM12878, the pre-trained MIX-HIC-Bimodal exhibits a standard deviation of just 0.0052—nearly three-fold lower than the 0.0151 of its non-pre-trained counterpart. This means pre-training yields far more reliable and consistent results when data is scarce. Furthermore, the full power of pre-training becomes most apparent in data-rich scenarios (Table 3), where it allows MIX-HIC to overcome the saturation seen in other methods by leveraging its comprehensive learned representations."}]

Author rebuttal:

Meta-review: This paper proposes MIX-HIC, a multimodal pretraining framework integrating Hi-C contact maps with epigenomic tracks to learn joint representations for 3D genomics. The model demonstrates strong performance on chromatin loop detection, contact map prediction, and gene expression tasks.

### Strengths
- First large-scale multimodal integration of Hi-C and epigenomics.
- Solid pretraining design with contrastive objectives and extensive ablations.
- Strong empirical results with robustness under noise and low coverage.
- Rebuttal addressed concerns with new analyses (e.g., interpretability, modality ablations, comparison with HiCFoundation).
- Open-sourcing data and code enhances community impact.

### Weaknesses
- Limited biological interpretability, though rebuttal added meaningful evidence.
- Evaluation restricted to a small set of cell lines and assay types.
- Comparisons to sequence-based models remain incomplete.

A technically sound and impactful paper that convincingly addresses reviewer concerns. The work is novel, well-executed, and of clear value to the AI4Science community. I recommend acceptance.
Decision: Accept (poster)