A New Paradigm for Genome-wide DNA Methylation Prediction Without Methylation Input
Authors: Xiaoke Huang
Qi Liu
Yifei Zhao
Xianfeng Tang
Yuyin Zhou
Wenpin Hou
Venue: ICLR 2026 Poster (2026)
Keywords: DNA Methylation
Deep Learning
Genome
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We develop a generalized gene-contextual transformer model for inferring whole-genome DNA methylation landscape without surrounding methylation as context information..

Abstract: DNA methylation (DNAm) is a key epigenetic modification that regulates gene expression and is pivotal in development and disease. However, profiling DNAm at genome scale is challenging: of $\textasciitilde$28 million CpG sites in the human genome, only about 1–3\% are typically assayed in common datasets due to technological limitations and cost. Recent deep learning approaches, including masking-based generative Transformer models, have shown promise in capturing DNAm–gene expression relationships, but they rely on partially observed DNAm values for unmeasured CpGs and cannot be applied to completely unmeasured samples. To overcome this barrier, we introduce MethylProphet, a gene-guided, context-aware Transformer model for whole-genome DNAm inference without any measured DNAm input. MethylProphet compresses comprehensive gene expression profiles ($\textasciitilde$25K genes) through an efficient bottleneck multilayer perceptron, and encodes local CpG sequence context with a specialized DNA tokenizer. These representations are integrated by a Transformer encoder to predict site-specific methylation levels. Trained on large-scale pan-tissue whole-genome bisulfite sequencing data from ENCODE (1.6 billion CpG–sample pairs, $\textasciitilde$322 billion tokens), MethylProphet demonstrates strong performance in hold-out evaluations, accurately inferring DNAm at unmeasured CpGs and generalizing to unseen samples. Furthermore, application to TCGA pan-cancer data (chromosome 1, 9,194 samples; $\textasciitilde$450 million training pairs, 91 billion tokens) highlights its potential for pan-cancer whole-genome methylome imputation. MethylProphet offers a powerful and scalable foundation model for epigenetics, providing high-resolution methylation landscape reconstruction and advancing both biological research and precision medicine.

Published as a conference paper at ICLR 2026
A NEW PARADIGM FOR GENOME-WIDE DNA METHY- LATION PREDICTION WITHOUT METHYLATION INPUT
Xiaoke Huang" Li Yifei Zhao?, Xianfeng Tang', Yuyin Zhou
Wenpin Hou Amazon Research
1 UC Santa Cruz 2 Columbia University
Duke University
indicates equal contribution, f indicates equal advising
ABSTRACT
DNA methylation (DNAm) is a key epigenetic modification that regulates gene expression and is pivotal in development and disease. However, profiling DNAm at genome scale is challenging: of -28 million CpG sites in the human genome, only about 1-3% are typically assayed in common datasets due to technological limitations and cost. Recent deep learning approaches, including masking-basce generative Transformer models, have shown promise in capturing DNAm- -nngene expression relationships, but they rely on partially observed DNAm values for unmeasured CpGs and cannot be applied to completely unmeasured samples. To overcome this barrier, we introduce Methy|Prophet, gene-guided, context-aware Transformer model for whole thole-genome DNAm inference without any measured DNAm input. MethylProphet compresses comprehensive gene expression profiles (-25K genes) through an efficient bottleneck multilayer perceptron, and encodes lo- cal CpG sequence context with specialized DNA tokenizer. These representations are integrated by a Transformer encoder to predict site-specific methylation levels. Trained on large-scale pan-tissue whole-genome bisulfite sequencing data from ENCODE (1.6 billion CpG-sample pairs, -322 billion tokens), MethylProphet demonstrates strong performance in hold-out evaluations, accurately inferring DNAm at unmeasured CpGs and generalizing to unseen samples. Furthermore, application to TCGA pan-cancer data (chromosome 1, 9.9,94 samples; -450 million training pairs, 91 billion tokens) highlights its potential for pan-cancer whole- genome methylome imputation. Methy/Prophet offers a powerful and scalable foundation model for epigenetics, providing high-resolution methylation landscape reconstruction and advancing both biological research and precision medicine.
INTRODUCTION
Imputation- based paradigm (e.g.. CpGPT, MethylGPT) Ours
CpG-wise ID prediction
CpG-wise OOD prediction w/o line-tuning
Unseen samples generalization w/o measured DNAm
Multi-omics prediction
Table 1: Paradiagm comparison.
DNA methylation (DNAm) is a key epigenetic modification that regulates gene expression, cell differentiation, and disease development (Feinberg) 2018 Loyfer et al. 2023). DNAm predominantly
occurs CpG pooosinephosspatteggee guanine) sites on the DNA sequence, whose -peuifc and dynamic nature makes them valuable Biomarkers (Hitz et al. 2023 The ENCODE Project Consortium] 2012 Luo et al. 2020 The Cancer Genome Atlas Research Network 2008). Despite its importance, comprehensive DNAm profiling remains prohibitive. Array-based platforms (e.g.. Illumina 450K/EPICC measure only small fraction (-1-3%) of the -28 million CpGs in the human genome, while whole-genon goole-genome bisulfite sequencing (WGBS) offers complete coverage but high cost (Shu et al. 2020). As a result, the majority of CpG sites remain unmeasured typical datasets (Figure 1 (a), Table T), limiting the insights
draw from DNAm data.

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Sample
APOC
CpG Site
Meceured DNAm
Unmeseured DNAm
Gene Profling
B90A1 OFTR
Prodicted DNAm
Query DNAm
Whole-genome Sequence
CpG-specific
Gene Expression
DNA Sequence
Uniassuued Mess Cy0
Imputation-
Methyl
based Model
CpG Site
Gene- Contextualized DNA Methylation Prediction
Prophet
WGBs
EPIC
imputation-bssed Prediction Paradigm
conexoootextuallzzed
DNAm Signal (Beta Value)
Prediction Paradigm
Figure (a) Illustration of the scale of DNAm data. Parts A, B, and existing DNAm samples. Part D: unmeasured CpG sites and new samples with gene expression measurements that users can apply MethylProphet to reliably predict their DNAm profiles. (b) Given gene context of a sample, MethylProphet aims to infer previous imputation- based methods (e.g., CpGPT (De Lima Camillo et al. (024)) MethylProphet does rely on experimentally measured DNAm obtained via wet-lab sequencing, and it directly predicts the target methylation for each CpG site, even those unknown CpG sites.
DNAm at individual CpG resolution.
(c) Compared with
Table 2: The scale of DNAm data included in this study.
Data Source
Sequencing Technique
CpG Sites
Tissues/ Cancers
# Samples
Total #CpGi-Sample
#CpG-Sample Pairs w/ DNAm
Pairs
ENCODE
WGBS
27.078,450
57
95
2,572.452,750
2,572.452,750
Array
408.399
33
9.194
3,754.820,406
3,684,770.088
TCGA
EPIC
740.296
1,706
1,262,944.99
1,188. 102,524
WGBS
23,047,052
17
737.505,
737.505, ,64
These limitations raise important question: Is it feasible infer DNAm profiles via deep learning models withoul performing additional sequencing or array experiments? Gene expression offers a promising source of complementary information, as numerous studies have revealed strong correla- tions between gene expression levels and DNA methylation patterns (Phillips et al., 20088 Jjingo et al. 2012). Importantly, gene expression data more readily available across tissues and conditions, and recent advances in large-scale foundation models for genomics suggest that rich biological signals can be learned from such high- -igeediiesiional data (Theodoris al. [023]] Cui al., 2024a Yang et al., 2022, Hao et al. 2024a). Motivated by this, we hypothesize that leveraging gene expression can dramatically reduce the reliance on extensive DNAm sequencing. alleviate data scarcity, and enable scalable inference of methylation landscapes.
We propose a novel paradigm for DNAm analysis: using a gene-contextual foundation model to pre- dict sample's 's methylome from its gene expression and DNA sequence context alone. This paradigm bypasses the need for any experimentally measured DNAm in the target sample, offering substantial practical advantages. It can lower costs and accelerate analyses by obviating wet-lab experiments, enable studies in resource-limited settings, and potentially reveal DNAm patterns that are otherwise undetectable due sparse measurements technical constraints. In line with recent successes in foundation models in genomics (e.g. Geneformer (Theodoris et al.) 2023), scGPT (Cui et al. 2024a), ScBERT (Yang et al.) 2022), and scFoundation (Hao et al. (2224a). we introduce MethylPropher to realize this vision. MethylProphet predicts DNAm by leveraging two key ingredients: (a) Bottleneck MIP to compress high-dimensional gene expression profiles (~25,0000 genes) into compact latent representation, enabling the model to capture global expression patterns and generalize to unseen samples; and (b) a DNA sequence tokenizer to encode local sequence context around each CpG site (e.g.. I kb window), capturing sequence motifs and epigenetic context that drive methyla- tion at unseen CpGs. These gene-derived and sequence-derive embeddings. along with additional genomic annotations (CpG island context, regional genomic features, chromosomal location), fused by a Transformer encoder /Vaswani 2017) to output the predicted methylation level for each CpG (Figure (b-c)). By fully leveraging gene expression as context, MethyiProphet infer
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sample's methylome without requiring any partial DNAm measurements. This capability stands in contrast to prior methods like DeepCpG (Angermueller et a 2017). CpGPT (De Lima Camillo et al. 2024), and MethylGPT (Ying et al. 2024), which all depend on some observed DNAm values during inference (imputation paradigm) and thus cannot handle entirely unlabeled samples. Moreover, unlike methods that focus limited CpG subsets (e.g., Mul Me--hyll (Zeng et al. 2022), StableD> NAm (Zhuo et al. 2023). and MethyINet (Levy et al. 2020),, Methy|Prophet provides genome-wide coverage, overcoming previous coverage limitations.
To rigorously evaluate Methy|Prophet, we compiled and processed two billion-scale DNAm datasets from ENCODE and TCGA (Table 2). The ENCODE dataset consists of whole-genome bisulfite
sequencing profiles (~27 million CpGs) across 95 normal samples (57 tissue types), yielding 1.6
billion CpG-sample training pairs (322 billion input tokens). The TCGA dataset includes 9,194 cancer samples (33 tumor types) with Illumina 450K array data (~400K CpGis, supplemented by EPIC (~740K CpGs) and WGBS (~23 million CpGs in 32 samples) to increase coverage; focusing on chromosome I, this provides 4550 million training pairs (91 billion tokens). We benchmark MethyIProphet in multiple prediction scenarios (with different combinations of unmeasured CpG sites and unseen samples) and observe strong performance particularly median across-sample Pearson correlation (MAS-PCC) for individual CpGs of 0.72 on ENCODE along with robust accuracy across diverse conditions in TCGA.
Our contributions include:
We develop a flexible and scalable encoding scheme that uniquely integrates gene expression profiles with local DNA sequence context predict DNAm, without requiring partially measured DNAm as in prior works, overcoming the major limitations of prior imputation- based methods.
Novel Paradigm:
Scalable Model Design and Benchmarking: We introduce a modular encoding framework combining an efficient Bottleneck MLP for gene compression with a specialized DNA sequence tokenizer. This design enables scalable training on billions of data points (e.g. 322B tokens from ENCODE, 91B from TCGA) while maintaining tractability and efficiency.
Generalization and Practical Impact: We demonstrate that MethylProphet achieves strong generalization performance across unmeasured CpGs and unseen samples. It attains high accuracy (median PCC ~ 0.7) on ENCODE and maintains robust performance across various prediction scenarios in TCGA. This foundation model paradigm for methylation inference opens the door to reconstructing complete methylomes from limited experimental data, with broad implications for epigenetic research and precision medicine
BACKGROUND AND RELATED WORKS
DNAm Data Scale and Coverage. DNA methylation (DNAm) can be represented as a CpG-by- sample matrix M Neya xN, where NCpa R 2.8 x 102 sites genome-wide and the entries are methylation levels (3 values E [0, 1)). Existing assays trade off coverage and cost: array-based platforms capture only 1-3% of CpGs (Rs 10S sites) (Shu al. 2020), while WGBS provides nearly complete coverage but remains costly for large cohorts. This disparity means that most CpGs are typically unmeasured in any given dataset, creating high-dimensional and massive missing data problem. A naive approach of assigning each CpG site a unique learnable parameter (embedding) in a model would be infeasible: it require ~6GB for all 2.8 x 10" CpGs with -o8-dimentional embeddings, and it fails to generalize to unseen CpGs (Figure T a)), Table
Gene Expression as Context. Gene expression can represented as Geamle matrix G E R NNN, where Ng ~ 20000 makes direct Transformer encoding intractable due to quadratic complexity of self-attention. However, capturing the full gene expression landscape is crucial, as provides global biological state that can inform local methylation states.
These characteristies demand novel architectural solutions that can effectively (1) represent and generalize across millions of CpG sites, and (2) efficiently process comprehensive gene expression profiles while maintaining computational tractability.
Detailed insights the biological implications are provided in the Appendix.
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b
Leomable Emtedcinng
OFTR NAHFF 1753
Sample Gene MLP Embedder
Gana Esstwotion Entedling
Gena Ecthertte tn Ertedding
Chromsorre Fretecter
Gane Expression
Diin
Chd
Qpc
Tokenizer Embedder
Cpt DNA
Transfomer Encoder
Cod
Proaeder
Mothylation
Intav
i
Signal (Beta Vaiun)
Ragie Type
Hit Eurd hirs!
Inout sequence
Eyd tecliennt
Oad isandil ince
Cad Idandd Indee Reyoe Type
Ragion Type
Rejion rot
RpG likee
Chromosome
Chromosome
Choorsann
Cap Ocsan
NCCA CGGAa CGA
BolleneekMLL Block
Toleraar
codae AAGIO Gi
Transformer Block
Bell-Mtn
Ling ond
FRN
Embediier
Gare
ReffeneccMM
GpG. Tokenirer
Transformrr
Transformr
Figure 2: Overview of our proposed pipeline. (a) Model architecture of MethylProphet; (b) The learnable Global, chromosome, and CPG island-related embeddings: (c) Model architecture of efficient gene profile compression MLP; (d) DNA Tokenizer for CpG-specific DNA sequence; (e) Model architecture of the Transformer encoder that aggregates all the embeddings.
Prior computational methods for DNAm imputation prediction have been limited in scope. Tra- ditional approaches and early deep learning models Zeng et al. 2022. Angermueller et al. 2017 Zhuo et al., 2023 Levy et al., 20200] Wang al,, 2024 Levy-Jurgenson
2019a) targeted DNAm prediction at limited subset of CpG sites (e.g. those on arrays or select regions) and/or specific sample sets. More recent Transformer-baser models (e.g. CpGPT (De Lima Camillo al. 2024). MethyIGPT (Ying al. 2024)) leveraged the power of attention mechanisms, but they employed masked modeling pre-training on the order of 10a CpGs (~0.03% of the genome) to learn latent representation of the methylome. Because their objective is to learn holistic representations for downstream tasks rather than directly predict missing values, these models still require some observed methylation input and do not generalize to completely unseen CpG sites many existing methods do not integrate critical biological context such as gene expression, or they face scaling bottlenecks when attempting to handle genome-wide patterns. Notably, Levy-Jurgenson et al.] (2019a) proposed a deep model with attention to predict methylation from gene expression and sequence, but it was only demonstrated on thousands of CpGs, limited sample size, and cancer cohorts.
samples. Furthermore,
In contrast, our work offers a comprehensive gene-contextual solution that addresses these gaps. MethylProphet distinguishes itself by operating at full methylome scale and by leveraging foundation-man model approach: it trained on billions of CpG- EpG-sample pairs to directly infer complete methylation profiles, enabling generalization to unmeasured CpGis and unseen samples in a way that prior methods could not achieve.
METHYLPROPHET MODEL
MethylProphet a cone-contextual Transfomer capable of learning the whole-genome DNAm land- scape by integrating genome-win wide gene expression with CpG-specific DNA sequence context. The model architecture (Figure 2 consists of distinct modules for encoding the sample's gene expression profile and the target CpGi site's context, which are combined within Transformer to produce
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methylation prediction. We adopt Transformer encoder (Vaswani 2017) because its self-attention natively captures the long-range dependencies that link distant CpG sites within kilobase- scale DNA sequences (De Lima Camillo al. 2024). seamlessly fuses heterogeneous embeddings of sequence, gene expression, and genomic annotations without bespoke cross-modality modules (Gao et al. 2024 Cui et al., 2024a| Yang al. 2022). In addition, it exhibits well-established scaling law (Kaplan et al., 2020; Henighan et al., 2020), i.e., more data consistently translate to better performance, making it an ideal backbone for whole-genome DNAm prediction.
Problem Formulation. Let g E R No denote the expression vector of Ng 25000 genes for a given sample, and let S, {A,T, C, G be the DNA sequence of length centered on CpG site i. Each CpG has auxiliary annotations a; (e.g., CpG island index, genomic region, chromosome). Our
goal is to learn a function
fe (G, St a.) + D E [0, 1],
(1)
where ij is the predicted DNAm level of CpG i, and y; is the ground-truth DNAm from sequencing.
Gene Expression Bottleneck MLP (Figure 2 (a, c): We employ bottleneck MLP (Bachmann et al. 2023) that compresses high-dimensional gene expression profile G into a compact latent embedding ggone E *R INNansa: Zgene = 6(W2 a ( + b1) + b2). where W1 E R. x,,, W2 E R R No, o is the GeLU activation, and è is layer normalization. Unlike approaches that attempted to attend only thousands of gene tokens (Cui et al. 2024b |Bai et al., 2024| Hao et al.] 2024b), this design (i) compresses ~ 25000 genes efficienlly, (ii) introduces minimal inductive bias, (iii) preserves long-range dependencies across the transcriptome, and (iv) generalizes to unseen samples.
CpG Sequence Tokenizer & Context Embeddings (Figure (a, d): To represent each target CpG site in a way that generalizes across millions of possible loci, we do not assign a fixed ID. Instead, encode CpG by its local genomic sequence context. We utilize DNA sequence tokenizer inspired by DNABERT-2 (Zhou et al.] 2024), which applies variable-length byte-pair encoding (BPE) scheme to the DNA sequence surrounding the CpG. Specifically, for each CpG site we take a window of e.g. 1000 base pairs (bp) centered on the site. This sequence S (length 1kb, consisting of characters A,T,C,G) is broken into a sequence of subword tokens T = t, via the DNA tokenizer (Figure 2 (a.d)). The tokenizer compresses repetitive or common motifs, achieving roughly 5x reduction in length (1,000bp ->~200 tokens) while preserving biologically relevant patterns. Each token 1, is then mapped to learnable embedding vector ...NNA RR-iineed This tokenization approach has several benefits: it identifies and reuses recurring sequence motifs (c.g.. CpG-rich patterns or regulatory motifs), reduces redundancy, and yields consistent embedding length for any CpG's context. Importantly, similar sequence patterns will produce similar token sequences, allowing the model to generalize knowledge across different CpG sites that share motifs. In addition to raw sequence, we incorporate genomic context features that help distinguish CpG sites:
CpG island (CGI) context (Figure 2 (a, b): DNAm behavior differs if CpG lies within CpG island, shore, shelf, or open sea (ocean). We include a CpG island index embedding to provide a unique identifier for each CpG island (with special index for non-island CpGi in open sea), as well as separate embeddings for region categories (island, shore, shelf, ocean). By summing the island-specific embedding with the region-type embedding, we obtain composite context vector TGG for the site. This encoding injects knowledge of local CpG density and regulatory regions, helping the model resolve ambiguity when similar DNA sequences appear in different contexts.
Chromosome indicator (Figure 2 (b)): We assign each chromosome a learnable embedding Tehr(k) R Notked for chromosome k (k 1,..., 22). This provides positional information that can capture chromosome-spettt -ppecccfic effects (such as varying methylation baseline or sequence composition) and helps the model differentiate sites that may have similar sequence but belong to different genomic compartments.
Global and integrated representations (Figure 2 (a)): Following conventions in Transformer models (Devlin) 2018) and prior genome foundation models(Cui et al. 2024b Bai et al., 2024 Hao et al. 2024b), we apply learnable global embedding token ØGLB- This vector does not correspond to any specific gene or CpG input; instead, it serves as an aggregate representation that can attend to all other embeddings. The Transformer can use TGLS to gather information across the gene expression context and the target CpG context. At the output, this global token't 's state will be fed into prediction head to produce the final methylation level for the CpG site in the given sample.
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Transformer Encoder and Prediction (Figure (a)): We concatenate all embedding vectors for given sample-CpC pair into a single sequence:
Z: TGLB Zgenn: [1 L 1 15=1 EOGL, Lchr ]
where Zegene is the gene expression embedding, 2; DNA are the DNA sequence token embeddings, Zeai encodes CpG island status, Fehr encodes the chromosome, and L is the DNA sequence length (default L = 2000). This sequence Zi forms the input to the Transformer encoder. It consists of stacked self-attention layers that fuse information across modalities. The bi-directional attention mechanism allows each token to attend to every other token in the sequence, enabling the model to combine global sample context with local site context effectively. For example, the gene expression embedding can influence how sequence tokens are interpreted and vice versa, allowing complex interactions (c.g. gene regulatory network effects on local methylation) to emerge in the learned representation. The Transformer encoder outputs contextualized embeddings of the same length. We then apply simple DNAm projector: a linear layer followed by a sigmoid activation, acting on the final state of the global token zar to predict the DNAm for the target CpG in the sample. This design, using a global token as the prediction carrier, is analogous to the "CLS" token in BERT models and encourages the network to integrate all information into that token for the final regression output.
Training Objective: MethyIProphet is trained end-to-end to minimize mean squared error (MSE) between predicted and true methylation values, with all components (gene MLP, tok- enizer embeddings, Transformer, etc.) updated via back propaaropag Train- ing fully supervised on large gene expression-DNAnn datasets, while in- ference requires only gene expression and sequence data. See Appendix for implementation and training details.
Sample
Train
Val
Training set
Train CpG Train Sample
Train
Three testing scenarios
1. CpG-wise ID Generalization
CpG site
Train CpG Val Sample
2. Sample-wise ID Generalization
Val CpG Train Sample
Seen data
3. OOD Generalization
Unseen data
Val CpG Val Sample
4 EXPERIMENTS
Figure 3: Methy|Prophet can predict unseen CpGs and un- seen samples without inputting measured DNAm levels. Each column is the genome-wide CpG-level DNAm of sample.
4.1 DATA SOURCE, PROTOCOLS, AND PRE-PROCESSING
We use ENCODE (Hitz et al. 2023 The ENCODE Project Consortium 2012 Luo et al. 2020 and TCGA data TThe Cancer Genome Atlas Research Network, 2008) for model evaluation. The ENCODE data contains 97 pairs of matched WGBS and RNA-seq samples across normal tissue types and cell types. We use for evaluating MethylProphet's performance on predicting pan-tissue whole- genome DNAm. The TCGA data contains 10,932 pairs of matched DNAm (including WGBS (W), EPIC 850K (E), and 450K array (A)) and RNA-seq samples. We use it for evaluating MethylProphet's performance on pan-cancer prediction. Together, they provide billions of DNA methylation signals at individual CpGs with matched gene expression profiles. To evaluate in- and out-of- generalization, we partitioned both samples and CpG sites, with train CpG-train -raitrain sample for training and the other three splits for validation (Figure Table 3). See Appendix for data and preprocessing steps.
4.2 BASELINE COMPARISONS
We evaluated Methy/Prophet's performance on all three validate splits (Figure
3
To quantify
agreement between predicted and measured DNAm values, we employ the following metrics (more analysis is provided Appendix): (a) Median- CCr Pearson correlation coefficient (MAC- PCC) assesses how well the model preserves each sample's overall DNAm profile.
(b) Median- across-sampp sample PCC (MAS-PCC) evaluates the model's 's ability to infer DNAm behavior at individual CpG sites. (c) Mean square error (MSE) evaluates the difference between predicted and measured DNAm. (d) Mean absolute error (MAE) evaluates the absolute difference between predicted and measured DNAm.

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Table 3: The data statistics among all the data souree and splits in our experiments. The number of tokens is estimated by the average sequence length (i.e, 200) of the input embeddings of the Transformer encoder.
Dataset
Sequen.
Split
CpG Sites
Samples
Tokens
Train: Train CpG Train Sample
24, 363, 170
57
66
1,607, 969, 220
321, 893, 814, D00
ENCODE
WGBS
Val CpG Train Sample
Train CpG Val Sample
24, 363,
707,
96
706. 9300
178. 664, 178
35. T32.
396,000
Val: Val CpG Val Sample
707,033 033
22
29
78, 28,5003 957
15, 700, 791, 400
Amay
EPIC
Trin: Train CyG Train Sample
71,748
4
7,76
115, 856, 100
23. 171, DO0
TCGA
WGBS
CpO Sample
989, 446
33,
32
63. 982, 272
12. 796, 454,
Army
Val: Val CpG Train Sample
6. 742
8,258
$5, 141,908
11, 11,028, 261, 600
Val: Val CpG Val Sample
6, 742
9120
6, 143, 360
222, 672, 000
Aray EPIC
2,38
632,, 281,
4066,
Train: Train CpG
Train Sample
172. 722
4
1,766
276. 181, 739
56, 236, 317, 500
TCGA
WGBS
Thain CnG Sample
5, 396, 193
78,
92
172, 672.678, 176
70,
34, 535,535,00
14,
Array
Val: Val CpG - Train Sample
14, 893
33
8, 8,28
121. 617, 682
24. 823, 536, 100
Val: Val CpG Val Sample
14,893
920
13, 550,550,000 097
2.710, 019,
We applied all metrics when benchmarking MethylProphet performance. the comparisons with CNN-based attention model @Levy-Jurgenson et al. 2019b). Methy|Prophet consistently outperforms this baseline across all settings (Table 5).
peathppPrppheepprfffmmaacce Metrics (a)-(d) were applied
Table 4: Performance comparison on ENCODE data. Four evaluation metries are included: median- across-sample PCC (MAS-PCC), median- acrols PCC (MAS-PCC) mean square error (MSE), and mean absolute error (MAE).
Tran CyG
Val Sample
Val CpG
Train Sample
Val CpG
Val Sample
MAS-
MAC-
MAS-
MAC-
MAS-
MAC-
PCC
PCC
MSE
MSE
Levy-Jurgensee et al.
20196
0.2878
0.3355
0,182
D.875
0.5453
0.7959
D.345
D.1250
0.1930
0.0077
D.043
0.1262 0.004
Menhyiroophar
0.3436
0.9.8
0.0079
0.M08
0.7165
0.9297
00T08
B0079
0.3411
0.330
0.0086
Table Performance comparison on TCGA data.
Train Cpti
Sample
CpG MAC- PCC
Val Sample
Cpti
Sample
MAS-
PCC
MAC-
PCC
MSK
MAR
MAS-
PCC
MSE
MAR
MAS-
PCC
MAC-
PCC
MSE
MAE
Sample buselinc Levy- Jurgesson et al. 2019
N/A
0,2630
0.6323
0.874
0.2148
0.2203
0.6633
OUBIS
0.2048
02158
0.562
0.0851
0.2055
Mcciyt/oophcc
0.5155
0.9320
0.0199
0.8822
0:0191
0.9065
0,0266
0.1000
0.300
0.9059
0J221
0.101
In addition, although CpGPT and MethyIGPT rely on a different paradigm and either do not predict for unseen samples, evaluated their in in-distribution generalization. MethylProphet achieves the highest MAS-PCC and MAC-PCC (Table
Table 6: Comparisons on "Val CpG Train Sample" split (in-distribution inference) across models.
ENCODE
TCGA chrl
Model
MAS-PCC
MAC-PCC
MAS-PCC
MAC-PCC
DeepCPG
0,0317
0.5560
-0.0080
0.4237
CpGPT
0.3192
0.9401
0.4794
0.9250
MethylGPT
0.2964
0.8953
0.4358
0.8357
MethylProphet
0.3689
0.9400
0.5453
0.9253
4.3 ANALYSIS PROCEDURE
We apply MethylProphet to predict DNAm the TCGA and ENCODE datasets, cross-validate its predictions, and analyze their biological implications. Specifically, CGI coherence is evaluated by measuring correlation between CpG pairs within the same CGL. As baseline, we compute correla- tions after randomly permuting CGI indicators. Uniform Manifold Approximation and Projection
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for Dimension Reduction (UMAP) (Leland Mclnnes 2018) verifies whether the inferred DNAm landscape captures tissue and cancer differences while preserving variation.
Methy]Prophet performance on ENCODE data The across CpG PCC (Figure 4 a. d)) reaches highest in the Train CpG Val Sample split, indicating that the model effectively captures site-wise DNAm patterns while generalizing well to new samples. If sample exhibits high across-CpC PCC, it suggests that the within-sample variability of CpGs is well captured (Figure (d)). This result is expected, as the overall DNAm profile of a sample consists of a long vector of CpG elements, and global trends in DNAm are typically easier to learn and predict. For across-sample PCC (Figure (b)), we observe a large variability, particularly when generalizing to unmeasured CpGs and unseen samples. The CpGs with high across-sample PCC indicate that the model can predict the CpG's variability across samples (Figure 4 (e)) well. This is very important because the ability to predict CpG's behavior across individuals highly related to its potential to identify a therapeutic target.
Specifically, the predictions are not only accurate when generalizing to unmeasured CpGs which a task that existing methods such as CpGPT can do, but MethylProphet also achives satisfactory performance in unmeasured samples (samples not seen at all for the model, and samples without experimentally- measured DNAm) which existing methods cannot do (Figure (d. e)). For across- CpG PCC (Figure 4 (a. d)), the performance is similar across splits, while for across-sampll PCC (Figure b. e)), Methy|Prophet performs best in the Val CpG Train Sample split, possibly due to the limited testing samples in ENCODE data. Further investigation show that the predictions are more accurately for highly variable CpGs, where across-sample PCC increases with CpG variability. In this normal tissue cohort, MethylProphet also effectively captures CpG co-methylation dynamics within CGIs (Figure (c)). In addition, MethylProphhs performs comparably across splits, likely due to the significantly large number of CpGs. Overall, MethylProphet successfully preserves tissue differences (Figure (f)), with predicted and measured samples of the same tissue types clustering together.
a
example sample
An example CpG
1.0
DNAm corndiatio within CGI
Predicted DNAm
0.5
Predicted DNVM
08-
088
042
04
0.00
0.2
-0.50
0.0
0.0
67
04
3.6
0.8
10
00
02
04
06
Moasured Methy|Prophet
Validation split:
Measured DNAm
Validtion split:
Measured DNAm
Valiation split:
Sample (Across CpG PCC+0.96)
Train CpG Vail Sample (Across sample PCC-0.56)
Train CpG Val Sample
Val CpG - Val Sample (Across- CpG PCC=0.91 92)
Val CpG Tran Sample (Across-sample PCC-0.84)
CpG Sample
CpG Sample (Acroes-eample PCC-0.64)
Val CpG Vail Sample
Adoas-Qa Qpa PCC
0:23
paross-cample PCC
12
0.30
UMAP1
105
0.50
0.0
0.70
Train Opo
Val Sample
Val CpG Train Sample
Var CnG Val Sample
Train Cua Vol Comple
Var GgG Train Sample
Ver Cng
Val Siample
UMAP2
Train CpG
Sample
Vall
Train Sample
CpG Val Sample
Measund
Preditted samples
Figure 4: Cross-validation results on ENCODE data. (a) An example sample to demonstrate the calculation of across- CpG PCC. (b) An example CpG to demonstrate the calculation of across sample PCC. (c) Predicted signal similarity within CGIs. (d) Across-CpC PCC. (e) Across-sample PCC. (f) UMAP of measured (triangles) and predicted (circles) samples.
MethyIProphet performance on TCGA data Evalution results demonstrated MethylProphet's strong pan-cancer prediction performance (see Appendix).
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4.4 ABLATION STUDIES
Data mixing strategies and scaling effects In the data mixing ablation (Table Methy|Prophet demonstrates strong CpG encoding capability ENCODE WGBS data, achieving high MAS- PCC scores of 0.72 on the Val CpG Train Sample split. However, performance on Val Sample is moderate, likely due to the limited sample size (Table 3) constraining the model's ability to learn generalized gene encodings for sample-specific features. Training solely on Array data yields suboptimal performance, with MAS-PCC scores of 0.40, 0.28, and 0.26 Train CpG Val Sample, Val CpG Train Sample, and Val CpG Val Sample splits, respectively. This limitation stems from the restricted number of CpG sites in Array data (Table Performance improves consistently when incorporating additional data sources, with optimal results achieved by combining Array, EPIC, and WGBS data, yielding MAS-PCC scores of 0.54, 0.42, and 0.39 for the respective splits.
Table 7: Results of training models different data sources. Datasets: E and T denotes ENCODE and TCGA, respectively; in brackets, A, E, and W denote Array, EPIC, and WGBS samples, respectively.
Train CpG
Val Sumple
Val CpG
Train Sample
Val CpG
Val Sample
Train Data
Val Data
MAS-
MAC-
MAS-
MAC-
MAS-
MAC-
POC
PCC
MSE
PCC
MAE
PCC
PCC
MSE
MAE
E(W)
E(W)
0.3436
0,9398
0.0079
0.0608
0,7168
0,9297
0.0108
0.0679
0.3411
0.9330
0.0086
0.0634
T(A)
T(A)
0.4000
0.8669
0.0363
0.1216
0.2769
0.7914
0.0555
0.1498
0.2597
0.7930
0.0557
0.1504
T(A+W)
T(A)
0.4205
0.3244
0.2981
0.9112
0.8674
0,8673
0.0252
0.0365
0.0369
0,1006
0.1205
0.1212
T(A+E)
T(A)
0.9232
0.0222
0.3727
0.8738
0.0350
0.1147
0.3451
0.0355
0.1157
T(A+E+W)
((A)
0.5455
0.9520
0.0199
0.0882
0.4194
0,9065
0.0266
0,1000
0,3904
0.9059
0.0271
0,1011
The scaling ablation (Table 8 reveals that models trained exclusively on chromosome show limited
generalization to additional chromosomes (2 and 3). While training on all three chromosomes slightly decreases validation performance on chromosome 1, it significantly improves the model's ability to generalize across chromosomes.
Table 8: The ablation of increasing training data scale by adding chromosomes for TCGA data.
Train Chr.
Val Chr.
Train CpG
Val Sample
Val CpG
Train Sample
Val CpG
Val Sample
MAS- PCC
MAC:
MAS-
MAC:
MAS-
MAC-
PCC
MAE
PCC
PCC
MSE
MAE
PCC
PCC
MSE
MAE
0.5455
0.9320
0.0199
0.0882
0.4994
0.9065
0,0266
0.1000
0.3904
0,9059
0.0271
0.1011
1+2+3
0.4928
0.9249
0.0219
0.0915
0.3760
0.8961
0,0294
0.1047
0,3505
0.8960
0.0298
0.1057
T+2+3
0.3025
0.8012
0.0335
0.1473
02234
0.8266
0,0492
0.1362
0,2513
0.224)
030455
0.1388
1+243
142+3
0.4872
0.9246
0.0224
0.0919
0.3736
0.8993
0.0290
0.1027
0,3460
0.8992
0.0295
0.1037
Contribution of gene encoding compeees MethylProphet with alternative gene encoding strategies, as well as a variant without gene encoding, on TCGA data. In the gene-pathway encoder, the Bottleneck MLP is replaced with a sparse MLP (Jaume et al.) (2024). Across all evaluation metrics, MethylProphet consistently achieves the best performance. See Appendix for implementation details.
Table 9: The ablation of different gene encoding strategies for TCGA data.
Train CpG
Val Sample
Val CpG
Val Sample
Val CpG
Val Sample
Cases
MAS-
MAC-
MAS-
MAC-
MAS-
MAC-
PCC
MSE
MAE
PCC
PCC
MSE
MAE
PCC
MSE
MAE
gene-parhway encoder
0.5371
0.9256
0.0212
0.0907
0.4194
0.9043
0.0271
0.12
0,3959
0.9018
0.0280
0.1029
DNA-soq encoder only
N/A
0.8539
0.0413
0.1393
N/A
0.8607
0.0400
0.1304
N/A
0.8607
0.0404
0.1311
MethyIPiophet
0,5455
0.9320
0.0199
0,08822
0.4194
0,9065
0.0266
0.1900
0.3901
0.9059
0.0271
0.1011
Contribution of genomic annotations Table I0 compares MethylProphet with and without genomic annotations (i.c., CpG island (CGI) annotation and chromosome identity).
4.5 COMPUTATIONAL EFFICIENCY AND PRACTICAL DEPLOYMENT
MethyIProphet is designed for practical deployment, requiring modest GPU memory and runtime. Table [T] shows benchmarks on NVIDIA L40s GPU (48GB), confirming that real-world applications feasible without heavy hardware demands.

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Published as a conference paper at ICLR 2026
Table 10: The ablation of genomic annotations (i.e., CGI and chromosome) for MethyIProphet. Datasets: E and T denotes ENCODE and TCGA, respectively; in brackets, A, E, W and digit denote Array, EPIC, and WGBS samples, chromosome No., respectively.
Tran Dea
Val Data
Mostel
MAS
Traas CyG-
MAC
Vill Sumple
MSE
MAE
STAS-
PCC
Ca Tran Sangle
KAA
MSE
MAE
KEAT"
Vul Sunple
MSE
MAE
Meclly/hopptt
06776
ROW
Marhylhhuphet choorroeooe
0.330
0.8116
0.838
0.888
0.99%
0.921
0.0117
0.088
0.333
09932
0,0500
0/960
NOWS
ROW
Marbrlhuppee
0.381
04418
0.870
0888
6316
0.809
0.008
0.678
0.881
89730
0,0066
TOLD
03333
03138
03021
0235%
03833
0393
0.033
0.140
0.782
03943
00000
0.122
D
0.445
6419
Table 11: Inference benchmarks of Methy/Prophet L.40s GPU (48GB). Each benchmark
2.7 x 102 CpG sites across 10 samples.
Number of Samples
Number of CpGs
Time
GPUs
Batch size
Memory
10
2.7 x 107
~9 min
32
256
12.3 GB
10
2.7 107
17 min
16
256
12.3 GB
10
2.7 x 107
34 min
8
256
12.3 GB
10
2.7 107
68 min
4
256
12.3 GB
CONCLUSION
We present Methy MethylProphet, novel Transformer-based approach that enables whole-genome DNA methylation inference by integrating gene profile with genomic context. Trained on extensive datasets, our model demonstrates robust performance in inferring genome-wide methylation patterns across diverse tissues and cancer types. We hope this capability reconstruct complete methylomes from limited experimental data could advance both epigenetic research and precision medicine applications.
ACKNOWLEDGEMENTS
W. H., Q. I. and Y.F. Z. were supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number K99/R00HG01 1468, through which W. H. received related training from Drs. Hongkai Ji, Stephanie Hicks, and Andrew Feinberg. Q. L. was also partially supported by the General Fund at Columbia University Department of Biostatistics. would like thank Columbia HPC (including Insomnia and C2B2), TPU Research Cloud (TRC) program and Google Cloud Research Credits program for supporting our computing needs. This research was partially supported by the UC National Laboratory Fees Research Program of the University of California Office of the President (Grant L,2CRR 10102..
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Appendix for "MethylProphet"
Contents
Potential Biological Insights
15
Correlation between DNAm and Gene Expression
15
DNA Methylation Provides Complementary Prognostic Information Gene Expression
16
D Data
16
D.1 Data Source
16
D.2 Data Partition and Protocols
17
D.3 Data Pre-Processing
18
Implementation Details
19
Configurations of Methy|Prophet
19
Baselines
19
E.2.1
Levy-Jurgenson al. (2019b)
19
E.2.2
CpGPT (De Lima Camillo et al. 2024)
20
F
Additional Evaluation Metrics
20
Across-Sampia PCC by DNAm Variability
20
F2
of Cell-Type and Tissue Differences
20
DMR Overlapping Proportion between Measured and Predicted Values
20
Evaluation Results
20
Robustness to Missing Context DNAm
20
MethylPropher Performance on TCGA Data
G.3
MethylProphet Performance ENCODE Data
23
Discussion
24
1
Limitations and Future Works
24
22
Broad Impact
24
I
The Use of Large Language Models (LLMs)
24
Reproducibility Statement
24
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A POTENTIAL BIOLOGICAL INSIGHTS
METHYL.PROPHER enables genome-wide DNA methylation (DNAm) reconstruction from gene expression and sequence data alone, providing unique opportunities for advancing biological inter- pretation, methodological development, and genomic applications. This cross-modality prediction framework offers several important insights and use cases in real-world biomedical research.
First, METHYLPROPHET facilitates low-cost methylome reconstruction in settings where whole- genome bisulfite sequencing (WGBS) or array-based profiling is infeasible. Many large-scale transcriptomic datasets lack matching methylome profiles, and thus cannot be directly leveraged for epigenetic discovery. For example, the ENCODE consortium has generated 1,699 RNA-seq samples but only 211 I WGBS samples; the TCGA program includes more than 10,426 RNA samples but only 32 WGBS samples; and GEO hosts 241,014 RNA-seq samples but just 6,318 WGBS samples. By computationally inferring DNAm in these cohorts, METUYLPROPPIET enables downstream epigenetic analyses without the need for additional profiling.
enhances public and disease biobank resources such as GTEx, ENCODE, TCGA, and PCAWG by providing whole- whole-genome genome methylome predictions. This allows for deeper epigenetic insights, cancer subtype stratification, and biomarker discovery. Prior work, such as Yang et al. 2024). predicted DNAm from GT'X and multi-omics TCGA data, but their scope was limited Illumina EPIC array CpGs, covering only ~3% of the genome. By contrast, PETPIYLPRPPIET enables whole-genome prediction at more than 100x the sample scale, thereby extending coverage from 3% to 100% of the genome and broadening the landscape of epigenetic discovery.
Second,
Third, METHYL.PROPHER supports sample-level methylation estimation in multi-omic and single- cell studies, where DNAm data are often sparse or missing. This ability reconstruct complete sample-level methylomes from transcriptomic profiles enables downstream tasks such as DNAm regulation inference, cell-fate trajectory analysis, and multi-omic clustering, all without requiring cethylation-speciff assays.
In addition, METHYLPROOPET contributes to predictive biomarker development. For instance, 850K array-based methylation profiles have been used to predict brain metastases (Zuccato et al. 2025). By extending methylation reconstruction to the full genome, METHYI.RROPHET opens new possibilities for noninvasive biomarker discovery and risk stratification in cohorts that lack direct methylation assays.
Another important application is in the development of DNA methylation clocks for aging and disease phenotyping. Epigenetic clocks such as Horvath and GrimAge estimate biological age based on small number of CpGs, but their accuracy is limited by array coverage (1-3% of the genome). NEthYLPROPPET provides genome-wide methylation inference, improving both the resolution and accuracy of aging models. Furthermore, it enables biological age estimation in transcriptome- only cohorts, thereby expanding the reach of age-related biomarkers in large-scale population and longitudinal studies.
Beyond these applications, METIYYL PROPIIET establishes cross-modality prediction as a powerful paradigm in multi-omics. Cross-modal inference increasingly central to computational biology:
studies have predicted DNAm from expression Yang et al. 2024 Liu et al. 2024). chromatin acces- sibility from expression and DNA (Zhou et al. 2017). and gene expression from sequence (Avsec) et al. 2021). More recently, ALPHAAGEOOME leveraged such predictions for virtual perturbation analyses (Avsee et al.) 2025). These efforts collectively reduce experimental cost, enable retro- spective analyses existing data, and broaden the scope of multi-omic investigations, especially in disease contexts such as cancer, heart failure, and leukemia. Within this broader landscape, METHYLPROPHHE demonstrates that accurate genome-wite wide DNAm prediction from transcriptome and sequence data is both feasible and biologically meaningful, thereby opening new directions for integrative epigenomic discovery.
B CORRELATION BETWEEN DNAM AND GENE EXPRESSION
Differences in DNAm result in differences in gene expression. For example, using distinct tissues with paired bulk RNA-seq and WGBS data from ENCODE data portal, we found a negative relationship between the DNAm each CpG and the expression of its nearest gene (Figure
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2878; correlation=0.30
DNA Methylation
1.00
0.50
0.25
0.00
10
Gene Expression
Figure 5: An example to show DNAm and the nearest gene expression negatively correlated (Spearman correlation -0.30).
(a)
(b)
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Suntea Protaiityy
Sunvval Proeaiity
72-
Low Nisk
500
2002
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Figure 6: Kaplan Meier survival curves for TCGA-BRCA patients based gene-expression-only (a) and combined gene expression and DNAm signatures (b).
Kaplan-Meier
C DNA METHYLATION PROVIDES COMPLEMENTARY PROGNOSTIC INFORMATION TO GENE EXPRESSION
Although DNA methylation is known to be strongly correlated with gene expression, it remains unclear whether DNAm provides additional information beyond transcriptomic measurements alone. To directly address this question, we performed survival risk stratification analyses in TCGA-BRCA patients using gene expression alone and the combined gene expression and DNAm signatures. Patients are then stratified into high- and low-risk groups based on the median value of the risk score derived from the Cox proportional hazards model. Kaplan-Meier survival curves are generated for two risk groups, and assessed statistical significance using the log-rank test.
As shown in Fig. (6) compared with gene expression alone (log-rank p 0.018), the combined gene expression and ANAm signature led a more pronounced separation between the high- and low-risk groups, with substantially increased statistical significance (log-rank p 0.0003), indicating enhanced survival risk stratification after incorporating DNAm information.
D DATA
D.1 DATA SOURCE
ENCODE data. Processed RNA-seq (TPM) and WGBS (3 values) data were downloaded from The Encyclopedia of Elements (ENCODE) portal (https:/ /ww.. encodeorojectt stt.eeorrrject.or/// We identified wild-type samples with both RNA-seq and WGBS profiles, along with matched summary information including species, sex, age, tissue, and bioSample information. Technical replicates were combined by averaging their gene expression and their DNA methylation profiles. The averaged
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TPM values were -ranssfanfor after adding a pseudocount of 1. For WGBS data aligned to the hg19 genome, genome coordinates were converted to hg38 using liftover. Samples with WGBS data covering more than 80% of all CpG sites autosomes and chromosome X were retained. Finally, all CpGs located on chromosomes X and Y were removed. A total of 95 samples covering 28,301,739 CpG sites and 55,503 genes were included in the final dataset.
TCGA data. Processed RNA-seq (TPM), 450K array and EPIC (3 values) data were down- loaded from the Cancer Genome Atlas Program (TCGA) data portal
cancer gov/). Processed whole-genome bisulfite sequencing (WGBS) data (3 values) were downloaded from a static website provided by TCGA (https: wwtp///wwwwddd.m/ s3. amazonaws com/ com/directory www._iistnnnttaaaicioieo
TCGA WGBS hg38 html). For RNA-seq data, the TPM values were averaged for samples belonging to the same case. The averaged TPM values were logg-transformed after adding a pseudocount of 1. For 450K array and EPIC data, CpG sites with missing values across all samples were filtered out, and the B values were averaged for samples belonging to the same case. The WGBS data provided 3 values for each case. CpG sites with missing values across all cases were filtered out, and those located on chromosomes and Y were removed. The final dataset included 9,194 450K array samples covering 408,399 CpG sites, 1,706 EPIC samples covering 740,296 CpG sites, and 32 WGBS samples covering 23,047,052 CpG sites. Additionally, gene expression profiles spanning 60,660 genes were included for each sample.
D.2 DATA PARTITION AND PROTOCOLS
Our model takes CpG-related information and gene expressions inputs and predicts the methylation level for the given CpG site. Originally, there are three raw files to be processed, a raw methylation beta file, sample gene profile, and reference human DNA sequence template (hg38. The raw methylation beta profile consists of a matrix M x N, where there are M CpG sites and N samples, while the gene expression profile includes the expression of L genes for all samples N. The data partition pipeline is shown in Figure
1. Split Data Shards and Check Sanity
Spitt Data into Farquet Shards
Sampl Gane Contect Profie
CpG-Sammle Methylation
Check and Filter NaN entries
Check and Filter Dupizated Erires
CpG conrespnnding DNA Sequence
2. Split Samples
Cancer (TOGA) Types
Spllt Samplos By Tisque (ENCODE)
Spllt Training and Valication Semples per Typa
3. Spilt CpG Sites
Check index Sameness CpG-Sammpl Mothylation
Spitt Statistiize CpG per Chromssomm
and Spitt Training and Validation CpG per Chromosome
CpG Correspanding DNA Sequence
Gananale Tran-Vaidation Spils:
Save Split CpG.
Training CpG Training Sample
Semple Pairs
Valicatio CpG & Training Sample
Training CpG Validtionn Sample
Valication CpG & Validation Sample
Figure Data partition diagram.
Data sharding and sanity check. Since the raw methylation beta matrix is enormous, reaching an order of magnitude of billion (2.8 billion for ENCODE WGBS and 3 billion for TCGA Array), we first split the gigantic matrix into small shards. Sharding can leverage parallel computation and thus speed up the data pre-processing. We split the methylation beta matrix by rows (i.e., by CpG sites) where every 10k rows assemble shard file. During methylation matrix sharding, the corresponding DNA sequence for each CpG site in shard is saved simultancously using the reference human DNA sequence template (hg38). The window size of DNA sequence is 1Kb for the given CpG site. In addition, we filter out NaN entries and deduplicate genes and CpG sites.
Sample split. To split samples in to training and validation set, first count the number of samples for each tissue/ / cancer types (ENCODE WGBS, Figure g TCGA Array, Figure (9). Then we split the samples based on the types.
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Figure 8: Samples counts by tissue types in ENCODE data.
Figure Samples counts by cancer types in TCGA data.
There are 57 tissue types and 95 samples in total in ENCODE. For those tissues with more than one samples, We randomly sampled half of them as the validation samples. All the rest samples are used for the training set.
In TCGA, there are cancer types with 9194 samples summed up. We randomly choose 10% of the samples for each tissue type validation samples, and the rest are left for training. For those do not have cancer type assigned, we treat them as type "Unknown".
CpG split. We first check the methylaton matrix and the corresponding DNA sequence have the same CpG index. Then we statisticize CpG sites for each chromosome. We randomly pick 10% for CpG sites in each chromosome training CpG sites. For ENCODE, we temporarly sample another 10% as training split. While for TCGA, we use the rest 90% as training. We supplement TCGA with addition EPIC and WGBS data which have intersected with Array data.
CpG-sample split. The CpG sample splits are based on the previous sample and CpG splits. For ENCODE WGBS and TCGA Array, would have four splits, where the first split is used for training, and the rest three splits are used for validation and performance report:
1. "Training CpG and Training Sample", for training;
2. "Training CpG and Validation Sample", for validation;
3. "Validation CpG and Training Sample", for validation;
4. "Validation CpG and Validation Sample", for validation.
To further synergy the limited CpG sites in TCGA array data, additionally incorporate TCGA EPIC and TCGA WGBS data, which have no intersections with TCGA array data.
D.3 DATA PRE- PRE-PROCSSSING
CpG-specific DNA sequence. We extract the DNA sequence around the CpG site to represent the CpG site. The window size is 1Kb for each site. Besides, we record the CpG island index, well as
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its region types (CpG island, CpG shore, CpG shelf, and CpG ocean). For those sites in CpG ocean, we assign -1 as their CpG island index. We embed the above information numerically.
Gene expression. The RNA counts log2 transformed after adding pseudocount of 1. Genes with mean and standard deviation below the specified cutoffs (ENCODE: mean = 0.1, std 0.1; TCGA: mean 0.5, std 0.5) are filtered out. Mitochondrial, proline-rich and ribosomal protein genes are removed. As a result, 24,337 genes are retained in the ENCODE dataset and 25,017 genes the TCGA dataset. Note that both protein-coding and non-protein-coding genes are included prior to filtering. To mitigate batch effects, we apply the quantization technique (Cui et al. (024b) where the -ranffsanfo RNA counts are quantized based on their probability densities. The quantized values are then linearly mapped to the range [0, 1] to mitigate batch effects. The resulting gene expression vectors are subsequently encoded in the downstream model.
E IMPLEMENTATION DETAILS
E. CONFIGURATIONS OF METHYLPROPHET
The implementation details shown in Table 12 For the experiments on ENCODE WGBS and TCGA chromosome 1, 2, and 3, we use 64 GPUs with 512 batch size per accelerator, taking about 1 GPU day for each experiment. While for those on TCGA chromosome 1, we use GPUs with batch size 256, taking about half of GPU day for each experiment. We turn on gradient checkpointing to reduce memory usage and enable flah-attention to speed up attention operator. The parameters specification and their computational cost are shown in Table T3
Table 12: The implementation details.
Optimization
Optimizer
AdamW (0.9, 0.95) 1.00E-04
LR
LR Decay Ratio
10x
LR Decay
cosine
Weight Decay
1.00E-03
LR Warmup
Linear
Warmup steps
2000
Gradient Clipping
Data Epoch
Batch Size*
256/512
Accelerator Type
NVIDIA L40s
Accelerator
32/64
Training Precision
Mixed bfl6
Table 13: Model size and computation. Number of parameters includes the DNA tokenizer embeddings. f: FLOPs are estiamted with batch size equal 1.
Transformer Size
of Hidden Layers
Hidden Size
# Attention Heads
#f Params
FLOPs
Base
12
768
12
110M
104G
MLP Size
# of Hidden Layers
Hidden Size
Bottleneck Factor
of Params
FLOPs
1024
1024
70M
70M
E.2 BASELINES
F.2.1
LEVY-JURGENSON ET AL..
2019B)
We implement the model described in Jevy-JJrgenson et al. (2019b), which uses a multi-branch architecture with four subnetworks: convolutional neural network (CNN) branches that process
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DNA sequences around CpG sites, and two iitention-basse MLP branches that incorporate gene expression and CpG-gene distance, respectively. The outputs of all branches are concatenated and passed through final regression head predict DNAm levels. We use the original model structure as described in the paper. To ensure fairness, we apply the same input preprocessing and trained the same data splits as Methy|Prophet. Our reimplementation is based the open-source code available at: https:: httpp:///ithbb /gtphtb.tttthgggg/ wtt.//oo////naaa http:///aaiiinnGrrru///ttttttttt
E.2.2 CPGPT (DE LIMA CAMILLO ET AL. 2024
CpGPT is an imputation- based Transformer model trained via masked modeling on large-scale CpG methylation data. It learns context-aware representations of CpG sites by predicting masked methylation values based on the surrounding sequence. In our evaluation, we use the trained CpGPT- 100M model to extract sample-level embeddings for 20 randomly selected samples from the Train Sample set. These embeddings are then used to predict DNAm levels at the corresponding Val CpG sites for each selected sample, following the Val CpG -Train Sample evaluation split. We the publicly released trained model and inference code from: https: /github..t.. com/ htm//camillo. CpGPT
F ADDITIONAL. EVALUATION METRICS
To complement the main performance metrics, we provide more evaluations to better understand model behavior, particularly in capturing biologically meaningful DNA methylation (DNAm) signals.
F.1 ACROSS-SAMPLE PCC BY DNAM VARIABILITY
We stratify CpG sites into bins according to their inter-sample DNAm variability, computed as the standard deviation of beta values across samples. For each bin, we compute the distribution of across-sample PCCs between predicted and measured methylation levels.
F.2 PCC OF DNAM CELL-TYPE AND TISSUE DIFFERENCES
To assess the preservation of biological variation, we compare pairwise differences in average methylation levels between tissues cell types, calculated for predicted and measured data. For each tissue or cell-type pair, we compute the PCC between predicted and measured methylation differences across CpG sites. High correlations indicate that the model captures inter-tissue and inter-cell-type epigenetic distinctions.
F.3 DMR OVERLAPPING PROPORTION BETWEEN MEASURED AND PREDICTED VALUES
We identify Differentially Methylated Regions (DMRs) from both measured and predicted methylation matrices using the 1 imma R package. We rank DMRs by statistical significance and compute the overlap proportion between top-ranked regions from the predicted and measured DNAm matrices, across varying thresholds (e.g., top 1000, 2000 DMRs).
G EVALUATION RESULTS
G.1 ROBUSTNESS TO MISSING CONTEXT DNAM
To assess the reliance surrounding DNAm context, conducted ablation study by progressively reducing the percentage of available context CpG values for CpGPT. Table [4 and Table 15 report the performance across 200 held-out test samples.
When no surrounding context DNAm available, CpGPT and MethylGPT degenerate (their output variance collapses). and the PCC metric becomes undefined. In contrast, MethylProphet remains stable across all levels of context sparsity due to its reliance on gene expression and DNA sequence inputs, which independent of neighboring CpG methylation measurements.
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Table 14: MAS-PCC (median across samples) under different levels of available context CpGs.
% surrounding DNAm
CpGPT
MethylGPT
Methy]Prophet
100%
0.19
0.23
0.31
80%
0.21
0.18
0.31
60%
0.13
0.15
0,31
40%
0.09
0.12
0.31
20%
0.06
0.08
0.31
Table 15: MAC-PCC (median across CpGs) under different levels of available context CpGs.
% surrounding DNAm
CpGPT
MethylGPT
MethylProphee
100%
0.84
0.78
0.88
80%
0.88
0.69
0.88
60%
0.79
0.63
0.88
40%
0.69
0.54
0.88
20%
0.60
0.49
0.88
These results highlight that Methy[Prophet is not only competitive in predictive accuracy but also substantially more robust and generalizable in low-data or missing-data settings. This robustness is especially valuable for real-world applications where measured DNAm data may be sparse or unavailable.
G.2
METHYL. PERFORMANCE ON TCGA DATA
Figure 10land and Figure II illustrate the distribution of PCC for our ablation studies the effect of mixing TCGA data with different sequencing techniques. 2) the effect of increasing data scale of TCGA.
Both across-Cpd CpG PCC (Figure 12 (a. b)) and across-sample PCC (Figure 12 (c, d)) reach the highest values in the Train CpG Val Sample split, indicating that the model effectively captures site-wise DNAm patterns while generalizing well to new samples. Specifically, the predictions are consistently more accurate when generalizing to new samples rather than new CpGs compared with splits of Val CpG Train Sample and Val CpG Val Sample (Figure T2 (b)). If a sample exhibits high across-CpG PCC, it suggests that the within-sample variability of CpGs is well captured (Figure 12 (a)). This result is expected, the overall DNAm profile of sample consists of long vector of CpG elements, and global trends DNAm are typically easier to learn and predict. For across- sample PCC (Figure T2 (d)), observe large variability, particularly when generalizing both unseen CpGs and samples. The CpGs with high across-sample PCC indicate that the model can predict the CpG's variability across samples (Figure 12 (c)) well. This is very important because the ability predict a CpG's behavior across individuals is highly related to its potential as a therapeutic target.
TOGA
TCGA(A-W)
TOGA(AIE)
(CGA/A+E
ENCODE (W)
PGD actons Sampa
1.0
POC caress Casa
Seadi
Figure 10: The distribution of PCC across Sample / CpG on validation sets for TCGA chromosome data.
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1+2+3
Chr:1+2+3
Val:
Val: Chr.
Val: Chr. 1-213
Val: Chr. 1:213
PCG
POC
Tem
1000 The
26.00 The Noteene
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Figure 11: The distribution of PCC across Sample/ CpG when increasing TCGA data scale by adding more chromosomes.
example sample
1:00
Prodicted DNimm
Producted DNWmm
Across-eampp mouss--ammple PCC
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CpG Sample (Across-sampla 83)
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Val CoG val Sample (Across-sample POC-0. 38)
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3000
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Figure 12: Cross Cross-validation validation results TCGA chromosome data. (a) An example sample to demonstrate the calculation across-CpG PCC. (b) Across-CpG PCC in three validation splits. (c) An example CpG demonstrate the calculation of across-sample PCC. (d) Across-sample PCC in validation splits. (e) Across-sample PCC by DNAm variability different train/validation
splits, including Train CpG Val Sample. Val CpG Train Sample Val CpG Val Sample (f)
Predicted signal similarity within CGIs, with the same color scheme (c). (g) The PCC of DNAm cell-type differences obtained from predicted and measured values. (h-j) DMR overlapping proportion
between measured and predicted values. (k) UMAP of measured (triangles) and predicted (circles) samples.
We found that the across-sample PCC positively correlates with a CpG's variability across samples (Figure 12 (e)). Specifically, the highest median PCC values observed for CpGs with standard deviation (SD) in the range (0.25, 0.36], reaching 0.70 for Train-CpG hal-Sample, 0.63 for Val-CpG Train-Sample and 0.60 for Val-CpG Val-Sample.
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An eccemple sample
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CpG Sample (Aaross-sample PCC-0 84)
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Figure 13:
on ENCODE data. Similar to that of Figure 12 except that the results are
based on the validation on ENCODE data. The sample differences (g) were calculated by comparing tissue/cell types rather than cancer types.
MethylProphet successfully maintains intra-CGI correlation patterns across different validation splits (Figure (f), indicating regional epigenetic regulation.
(g)). The Train CpG Val Sample split exhibits the highest median PCC difference, indicating that model effectively maintains cancer-specific DNAm patterns when predicting new samples using fixed set of CpGs. However, the Val CpG Train Sample and Val CpG Val Sample splits show decline in PCC differences, suggesting reduced performance in capturing cancer-type variation when generalizing to unseen CpGs.
In addition, MethylProphet is able to preserve cancer-specific DNAm differences (Figure 12
The differential CpGs achieves the highest overlap between predicted and measured DNAm in the Train CpG - Val Sample split, followed by Val CpG Train Sample and Val CpG Val Sample splits (Figure 12 (h-j)). In addition, SeehylProphet-preiicee DNAm landscape successfully preserves cancer-specific differences, as samples from the same cancer type remain well-clustered (Figure (k)).
G.3 METHYLPROPHES PERFORMANCE ON ENCODE DATA
Unlike TCGA, where Methy|Prophet performs best in the Train CpG - Val Sample split, ENCODE
shows different trend across validation splits. For across-Cpe PCC (Figure 13 (a, b)). the per- formance is similar across splits, while for across-sample PCC (Figure 13 (c. d)), MethylProphet performs best in the Val CpG Train Sample split, possibly due to the limited testing samples EN- CODE data. Similar that in TCGA, MethylProphet predicts methylation patterns
accurately for highly variable CpGs, where across-sample PCC increases with CpG variability (Figure [3 (e)).
In this normal tissue cohort, MethylProphet also effectively captures CpG oo-methylation dynamics within CGIs (Figure (f). In the assessment of MethylProphet's ability to preserve tissue-specific DNAm differences, the Val CpG Train Sample split exhibits the highest median PCC-across-CpCC
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difference (Figure 13 (g)). This contrasts with TCGA, where the Train CpG Val Sample split performed best.
The top-ranked DMRs obtained using predicted and measured DNAm achieve relatively high overlap across all validation splits (Figure [3 (h-j)). However, unlike TCGA, MethylProphet performs comparably across splits. This suggests that the DMR list is more stable, likely due the significantly larger number of CpGs included in ENCODE data. Overall, MethylProphet successfully preserves tissue differences (Figure 13 (k)), with predicted and measured samples of the same cancer types cluster together.
H DISCUSSION
H.1 LIMITATIONS AND FUTURE WORKS
This work should be regarded as proof-of-coneept study that demonstrates the feasibility of leveraging gene expression and genomic context for whole-genome DNA methylation inference. While MethyiProphet introduces new paradigm and achieves promising results, we do not propose fundamentally new model architectures nor do systematically explore more efficient specialized designs. Instead, our focus is on establishing baseline feasibility and potential, rather than optimizing for computational efficiency architectural innovation. Future research could address these aspects by adopting alternative architectures or scaling strategies to further improve performance and resource efficiency.
H.2 BROAD IMPACT
While our primary objective is to enhance epigenetic research and precision medicine capabilities, we acknowledge that advances in genomic prediction technologies may have broader societal im- plications, including privacy considerations and ethical questions regarding genetic information accessibility. We have focused on developing methods that maintain scientific rigor while adhering to established ethical guidelines in computational biology and medical research. Our model, data source, data processing pipelines, and evaluation protocols are designed with transparency and reproducibility in mind, and we will release all code, data, protocols, and models to facilitate open scientific discourse and validation.
I THE USE OF LARGE LANGUAGE MODELS (LLMs)
During the preparation of this manuscript, we used OpenAl's GPT-5 model for minor language refinement and smoothing of the writing. The Al tool not used for generating original con- tent, conducting data analysis, or formulating core scientific ideas. All conceptual development, experimentation, and interpretation were conducted independently without reliance on AI tools.
J REPRODUCIBILITY STATEMENT
We full-stack release our data, code, protocols, and models in the pursuit of open science
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Reviews:
[{"review_id": "o6PMtitTi4", "reviewer": "Reviewer_SVbA", "summary": "This paper presents MethylProphet, a Transformer model trained to prediction DNA methylation from gene expression, DNA sequence, and genomic annotations (such as CpG island annotation). Their approach is novel because it allows for whole genome DNA methylation inference for samples without any measure DNA methylation sites.\n\nTheir results show that their model can highly accurately predict methylation status across CpG sites in the genome for unseen samples. However, the performance for predicting methylation status at individual CpG sites across samples is only moderate, even for CpG site seen during training.\n\nOverall, the results presented are interesting and well executed, but the paper lacks sufficient baselines and ablations to prove that their model beats simple imputation techniques.", "questions": "- I do not see any results showing generalization performance between datasets - was this tested?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper is generally well written, and the problem and approach are clearly presented.\n- The paper seeks to solve a problem that has not yet been tackled by other models in the field — whole genome methylation inference — and demonstrates that their approach is predictive.\n- The authors validate their results on two independent datasets and multiple different methylation sequencing assays. Their results are quite robust across datasets and assays\n- The validation splits and evaluation metrics are well designed.", "weaknesses": "In my opinion, the main weakness of this paper is a lack of sufficient baselines and ablations.\n- There is only a comparison to one baseline “the CNN-based attention model in Levy-Jurgenson et al. (2019b)” and only on one of their two datasets. Moreover, from the details given in the appendix, it seems that MethylProphet and the baseline are not trained with the exact same information. In particular, it seems that MethylProphet is trained with additional genomic annotations that the CNN is not. For a fair comparison, MethylProphet should be trained with the exact same features/data as the baseline\n- The point is taken that a main innovation of the model is that it can perform whole genome methylation inference, which cannot be done by many of the prior models in this space. However, it would still be worthwhile to see more comprehensive baselines against popular models in the field — such as CpGPT and DeepCpG — on the in-distribution generalization task. The results presented in Figure 5 are only against one baseline model and on one chromosome.\n- For the MAC-PCC metric, it would be valuable to include another simple baseline of the mean methylation status across training samples\n- There are minimal ablations performed on the components of the methylProphet model. In particular, CpG island context is highly indicative of methylation status, and an ablation should be performed to understand how much of the models performance can be attributed to this annotation alone.\n\nMinor: \nSome figures and methods could be updated for clarity. \n\n    - For example, in table 4 MAS-PCC and MAC-CC are not defined (although they are defined later in the text)\n    - In Figure 4, axes should be labeled with “samples” and “CpGs”. In addition, moving figure 4 earlier in the paper would be helpful", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FlJZ2DkVps", "reviewer": "Reviewer_Epkb", "summary": "This paper introduces MethylProphet, a proof-of-concept deep learning model that demonstrates the feasibility of predicting genome-wide DNA methylation using only gene expression and genomic context, without requiring any methylation data as input.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The methodological exposition is characterized by its clarity and high quality.\n2. The objective of integrating multi-modal information is both well-defined and effective.", "weaknesses": "The primary issue with this paper is that while its core innovation is the mDNA-free prediction, it overlooks existing methods capable of predicting methylation sites from either gene expression alone or DNA sequence alone. Therefore, it is essential to establish a comparative baseline to demonstrate the synergistic advantage of fusing both modalities (gene expression and DNA sequence) over relying on a single modality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4MeozWPrdv", "reviewer": "Reviewer_WQPP", "summary": "The paper introduces a Transformer-based model for predicting whole-genome DNA methylation profiles from gene expression and DNA sequence alone, without requiring measured methylation values. Trained on billion-scale ENCODE and TCGA datasets, it achieves strong performance in zero-shot methylation prediction across both unmeasured CpG sites and unseen samples.", "questions": "1. How sensitive is the model's performance to the 1kb sequence window size?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. This paper addresses a key limitation of imputation-based methods for DNAm prediction, allowing application to entirely new samples.\n\n2. The paper proposes a new formulation for the DNAm prediction task, which is intuitive and well-motivated.\n\n3. The authors conduct a comprehensive evaluation which includes multiple validation splits to test model’s generalization ability.\n\n4. The manuscript is well-written and well-structured, supported by informative results.", "weaknesses": "1. The paper does not clearly explain why this task should be feasible, especially why gene expression is used as an input modality.\n\n2. It would be better to include an analysis of which patterns of CpGs are predictable and which are not.\n\n3. Given the two types of input data, it is hard to know the contribution from each modality. The paper should include an ablation study comparing the model to sequence-only and expression-only baselines.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XMUGUlr2nL", "reviewer": "Reviewer_W98K", "summary": "The paper presents a transformer-based architecture to predict DNA methylation. The key innovation is represented by the use of \"paired\" gene expression profiles as sources of additional context, which enables prediction at the single CpG site even in the absence of information on methylation (but with expression measurements). The proposed approach, called MethylProphet, is tested on data from major consortia (ENCODE and TGCA) demonstrating a good level of accuracy.", "questions": "- Methylation arrays often provide an aggregated reading for nearby CpGs, how do you deal with that?\n- How does the model perform on special CpGs, e.g. the ones used in clocks?\n- How do you integrate coverage information in your loss function?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The paper is well written, the task is well motivated and the approach is described in sufficient detail as to be clear whet the authors are doing and why.\n- The topic is of interest, although with caveats (see weaknesses).\n- The idea of using gene expression as context is novel and certainly of relevance to the task.", "weaknesses": "I have many reservations about the paper, here are the main ones:\n- Gene expression and methylation are well known to be correlated, so in a sense adding expression as context is a natural idea. The question is whether adding expression makes the task still worthwhile. Much of the interest in methylation stems from its mechanistic role in regulating expression, as well as its biomarker function. If you already have expression measurements, the need to know much about methylation might be questionable.\n- Methylation is poorly understood in general but most CpGs are either unmethylated in islands associated with active genes, or methylated in the ocean or near repressed genes. This already suggests that knowledge of expression could provide a simple model with accuracy on a comparable scale (if in island and gene on, then 0, otherwise 1).\n- The comparisons are very limited, and the Fig 5 comparison against a method that does not use expression shows a fairly limited improvement despite the extra data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)