pwc-archive/papers-with-abstracts huggingface dataset:
DuAL-Net: A Hybrid Framework for Alzheimer's Disease Prediction from Whole-Genome Sequencing via Local SNP Windows and Global Annotations
Authors: Eun Hye Lee, Taeho Jo
Date: 2025-05-31 00:00:00
Tasks: Disease Prediction

Alzheimer's disease (AD) dementia is the most common form of dementia. With the emergence of disease-modifying therapies, predicting disease risk before symptom onset has become critical. We introduce DuAL-Net, a hybrid deep learning framework for AD dementia prediction using whole genome sequencing (WGS) data. DuAL-Net integrates two components: local probability modeling, which segments the genome into non-overlapping windows, and global annotation-based modeling, which annotates SNPs and reorganizes WGS input to capture long-range functional relationships. Both employ out-of-fold stacking with TabNet and Random Forest classifiers. Final predictions combine local and global probabilities using an optimized weighting parameter alpha. We analyzed WGS data from 1,050 individuals (443 cognitively normal, 607 AD dementia) using five-fold cross-validation. DuAL-Net achieved an AUC of 0.671 using top-ranked SNPs, representing 35.0% and 20.3% higher performance than bottom-ranked and randomly selected SNPs, respectively. ROC analysis demonstrated strong positive correlation between SNP prioritization rank and predictive power. The model identified known AD-associated SNPs as top contributors alongside potentially novel variants. DuAL-Net presents a promising framework improving both predictive accuracy and biological interpretability. The framework and web implementation offer an accessible platform for broader research applications.
Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
Authors: Taeho Jo, Eun Hye Lee, Alzheimer's Disease Sequencing Project
Date: 2025-05-31 00:00:00
Tasks: Classification, Robust classification

INTRODUCTION: Alzheimer's disease (AD) is genetically complex, complicating robust classification from genomic data. METHODS: We developed a transformer-based ensemble model (TrUE-Net) using Monte Carlo Dropout for uncertainty estimation in AD classification from whole-genome sequencing (WGS). We combined a transformer that preserves single-nucleotide polymorphism (SNP) sequence structure with a concurrent random forest using flattened genotypes. An uncertainty threshold separated samples into an uncertain (high-variance) group and a more certain (low-variance) group. RESULTS: We analyzed 1050 individuals, holding out half for testing. Overall accuracy and area under the receiver operating characteristic (ROC) curve (AUC) were 0.6514 and 0.6636, respectively. Excluding the uncertain group improved accuracy from 0.6263 to 0.7287 (10.24% increase) and F1 from 0.5843 to 0.8205 (23.62% increase). DISCUSSION: Monte Carlo Dropout-driven uncertainty helps identify ambiguous cases that may require further clinical evaluation, thus improving reliability in AD genomic classification.
JojoSCL: Shrinkage Contrastive Learning for single-cell RNA sequence Clustering
Authors: Ziwen Wang
Date: 2025-05-31 00:00:00
Tasks: Clustering, Contrastive Learning

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling gene expression analysis at the individual cell level. Clustering allows for the identification of cell types and the further discovery of intrinsic patterns in single-cell data. However, the high dimensionality and sparsity of scRNA-seq data continue to challenge existing clustering models. In this paper, we introduce JojoSCL, a novel self-supervised contrastive learning framework for scRNA-seq clustering. By incorporating a shrinkage estimator based on hierarchical Bayesian estimation, which adjusts gene expression estimates towards more reliable cluster centroids to reduce intra-cluster dispersion, and optimized using Stein's Unbiased Risk Estimate (SURE), JojoSCL refines both instance-level and cluster-level contrastive learning. Experiments on ten scRNA-seq datasets substantiate that JojoSCL consistently outperforms prevalent clustering methods, with further validation of its practicality through robustness analysis and ablation studies. JojoSCL's code is available at: https://github.com/ziwenwang28/JojoSCL.
A DNA Methylation Classification Model Predicts Organ and Disease Site
Authors: Keng-Jung Lee, Dharanya Sampath, Konstantinos Mavrommatis
Date: 2025-05-30 00:00:00
Tasks: Dimensionality Reduction, Imputation

Cell-free DNA (cfDNA) analysis is a powerful, minimally invasive tool for monitoring disease progression, treatment response, and early detection. A major challenge, however, is accurately determining the tissue of origin, especially in complex or heterogeneous disease contexts. To address this, we developed a machine learning framework that leverages tissue-specific DNA methylation signatures to classify both tissue and disease origin from cfDNA data. Our model integrates methylation datasets across diverse epigenomic platforms, including Whole Genome Bisulfite Sequencing (WGBS), Illumina Infinium Bead Arrays, and Enzymatic Methyl-seq (EM-seq). To account for platform variability and data sparsity, we applied imputation strategies and harmonized CpG features to enable cross-platform learning. Dimensionality reduction revealed clear tissue-specific clustering of methylation profiles. A random forest classifier trained on these features achieved consistent classification performance (accuracy 0.75-0.8 across test sets and platforms). Notably, our model distinguished clinically relevant tissues such as inflamed synovium and peripheral blood mononuclear cells (PBMCs) in arthritis patients and deconvoluted synthetic cfDNA mixtures mimicking real-world liquid biopsy samples. The predicted tissue proportions closely matched the true values, demonstrating the model's potential for both classification and quantitative inference. These results support the feasibility of using cross-platform methylation data and machine learning for scalable, generalizable cfDNA diagnostics and lay the groundwork for future integration of disease-specific epigenetic features to guide clinical decision-making in precision medicine.
PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset
Authors: Liangrui Pan, Qingchun Liang, Shen Zhao, Songqing Fan, Shaoliang Peng
Date: 2025-05-30 00:00:00
Tasks: Benchmarking, Multiple Instance Learning, whole slide images

Accurately predicting gene mutations, mutation subtypes and their exons in lung cancer is critical for personalized treatment planning and prognostic assessment. Faced with regional disparities in medical resources and the high cost of genomic assays, using artificial intelligence to infer these mutations and exon variants from routine histopathology images could greatly facilitate precision therapy. Although some prior studies have shown that deep learning can accelerate the prediction of key gene mutations from lung cancer pathology slides, their performance remains suboptimal and has so far been limited mainly to early screening tasks. To address these limitations, we have assembled PathGene, which comprises histopathology images paired with next-generation sequencing reports from 1,576 patients at the Second Xiangya Hospital, Central South University, and 448 TCGA-LUAD patients. This multi-center dataset links whole-slide images to driver gene mutation status, mutation subtypes, exon, and tumor mutational burden (TMB) status, with the goal of leveraging pathology images to predict mutations, subtypes, exon locations, and TMB for early genetic screening and to advance precision oncology. Unlike existing datasets, we provide molecular-level information related to histopathology images in PathGene to facilitate the development of biomarker prediction models. We benchmarked 11 multiple-instance learning methods on PathGene for mutation, subtype, exon, and TMB prediction tasks. These experimental methods provide valuable alternatives for early genetic screening of lung cancer patients and assisting clinicians to quickly develop personalized precision targeted treatment plans for patients. Code and data are available at https://github.com/panliangrui/NIPS2025/.
Scalable Generation of Spatial Transcriptomics from Histology Images via Whole-Slide Flow Matching
Authors: Tinglin Huang, Tianyu Liu, Mehrtash Babadi, Wengong Jin, Rex Ying
Date: 2025-05-25 00:00:00

Spatial transcriptomics (ST) has emerged as a powerful technology for bridging histology imaging with gene expression profiling. However, its application has been limited by low throughput and the need for specialized experimental facilities. Prior works sought to predict ST from whole-slide histology images to accelerate this process, but they suffer from two major limitations. First, they do not explicitly model cell-cell interaction as they factorize the joint distribution of whole-slide ST data and predict the gene expression of each spot independently. Second, their encoders struggle with memory constraints due to the large number of spots (often exceeding 10,000) in typical ST datasets. Herein, we propose STFlow, a flow matching generative model that considers cell-cell interaction by modeling the joint distribution of gene expression of an entire slide. It also employs an efficient slide-level encoder with local spatial attention, enabling whole-slide processing without excessive memory overhead. On the recently curated HEST-1k and STImage-1K4M benchmarks, STFlow substantially outperforms state-of-the-art baselines and achieves over 18% relative improvements over the pathology foundation models.
Geometric Hyperscanning under Active Inference
Authors: Nicolas Hinrichs, Mahault Albarracin, Dimitris Bolis, Yuyue Jiang, Leonardo Christov-Moore, Leonhard Schilbach
Date: 2025-06-10 00:00:00

Second-person neuroscience holds social cognition as embodied meaning co-regulation through reciprocal interaction, modeled here as coupled active inference with affect emerging as inference over identity-relevant surprise. Each agent maintains a self-model that tracks violations in its predictive coherence while recursively modeling the other. Valence is computed from self-model prediction error, weighted by self-relevance, and modulated by prior affective states and by what we term temporal aiming, which captures affective appraisal over time. This accommodates shifts in the self-other boundary, allowing affect to emerge at individual and dyadic levels. We propose a novel method termed geometric hyperscanning, based on the Forman-Ricci curvature, to empirically operationalize these processes: it tracks topological reconfigurations in inter-brain networks, with its entro-py serving as a proxy for affective phase transitions such as rupture, co-regulation, and re-attunement.
The Predictive Brain: Neural Correlates of Word Expectancy Align with Large Language Model Prediction Probabilities
Authors: Nikola Kölbl, Konstantin Tziridis, Andreas Maier, Thomas Kinfe, Ricardo Chavarriaga, Achim Schilling, Patrick Krauss
Date: 2025-06-10 00:00:00
Tasks: EEG, Language Modeling, Language Modelling, Large Language Model

Predictive coding theory suggests that the brain continuously anticipates upcoming words to optimize language processing, but the neural mechanisms remain unclear, particularly in naturalistic speech. Here, we simultaneously recorded EEG and MEG data from 29 participants while they listened to an audio book and assigned predictability scores to nouns using the BERT language model. Our results show that higher predictability is associated with reduced neural responses during word recognition, as reflected in lower N400 amplitudes, and with increased anticipatory activity before word onset. EEG data revealed increased pre-activation in left fronto-temporal regions, while MEG showed a tendency for greater sensorimotor engagement in response to low-predictability words, suggesting a possible motor-related component to linguistic anticipation. These findings provide new evidence that the brain dynamically integrates top-down predictions with bottom-up sensory input to facilitate language comprehension. To our knowledge, this is the first study to demonstrate these effects using naturalistic speech stimuli, bridging computational language models with neurophysiological data. Our findings provide novel insights for cognitive computational neuroscience, advancing the understanding of predictive processing in language and inspiring the development of neuroscience-inspired AI. Future research should explore the role of prediction and sensory precision in shaping neural responses and further refine models of language processing.
Less is More: some Computational Principles based on Parcimony, and Limitations of Natural Intelligence
Authors: Laura Cohen, Xavier Hinaut, Lilyana Petrova, Alexandre Pitti, Syd Reynal, Ichiro Tsuda
Date: 2025-06-08 00:00:00

Natural intelligence (NI) consistently achieves more with less. Infants learn language, develop abstract concepts, and acquire sensorimotor skills from sparse data, all within tight neural and energy limits. In contrast, today's AI relies on virtually unlimited computational power, energy, and data to reach high performance. This paper argues that constraints in NI are paradoxically catalysts for efficiency, adaptability, and creativity. We first show how limited neural bandwidth promotes concise codes that still capture complex patterns. Spiking neurons, hierarchical structures, and symbolic-like representations emerge naturally from bandwidth constraints, enabling robust generalization. Next, we discuss chaotic itinerancy, illustrating how the brain transits among transient attractors to flexibly retrieve memories and manage uncertainty. We then highlight reservoir computing, where random projections facilitate rapid generalization from small datasets. Drawing on developmental perspectives, we emphasize how intrinsic motivation, along with responsive social environments, drives infant language learning and discovery of meaning. Such active, embodied processes are largely absent in current AI. Finally, we suggest that adopting 'less is more' principles -- energy constraints, parsimonious architectures, and real-world interaction -- can foster the emergence of more efficient, interpretable, and biologically grounded artificial systems.
Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example
Authors: Yuhan Helena Liu, Guangyu Robert Yang, Christopher J. Cueva
Date: 2025-06-07 00:00:00

Understanding how the brain learns may be informed by studying biologically plausible learning rules. These rules, often approximating gradient descent learning to respect biological constraints such as locality, must meet two critical criteria to be considered an appropriate brain model: (1) good neuroscience task performance and (2) alignment with neural recordings. While extensive research has assessed the first criterion, the second remains underexamined. Employing methods such as Procrustes analysis on well-known neuroscience datasets, this study demonstrates the existence of a biologically plausible learning rule -- namely e-prop, which is based on gradient truncation and has demonstrated versatility across a wide range of tasks -- that can achieve neural data similarity comparable to Backpropagation Through Time (BPTT) when matched for task accuracy. Our findings also reveal that model architecture and initial conditions can play a more significant role in determining neural similarity than the specific learning rule. Furthermore, we observe that BPTT-trained models and their biologically plausible counterparts exhibit similar dynamical properties at comparable accuracies. These results underscore the substantial progress made in developing biologically plausible learning rules, highlighting their potential to achieve both competitive task performance and neural data similarity.
Bio-Inspired Classification: Combining Information Theory and Spiking Neural Networks -- Influence of the Learning Rules
Authors: Zofia Rudnicka, Janusz Szczepanski, Agnieszka Pregowska
Date: 2025-06-07 00:00:00
Tasks: Classification, Computational Efficiency

Training of Spiking Neural Networks (SNN) is challenging due to their unique properties, including temporal dynamics, non-differentiability of spike events, and sparse event-driven activations. In this paper, we widely consider the influence of the type of chosen learning algorithm, including bioinspired learning rules on the accuracy of classification. We proposed a bioinspired classifier based on the combination of SNN and Lempel-Ziv complexity (LZC). This approach synergizes the strengths of SNNs in temporal precision and biological realism with LZC's structural complexity analysis, facilitating efficient and interpretable classification of spatiotemporal neural data. It turned out that the classic backpropagation algorithm achieves excellent classification accuracy, but at extremely high computational cost, which makes it impractical for real-time applications. Biologically inspired learning algorithms such as tempotron and Spikprop provide increased computational efficiency while maintaining competitive classification performance, making them suitable for time-sensitive tasks. The results obtained indicate that the selection of the most appropriate learning algorithm depends on the trade-off between classification accuracy and computational cost as well as application constraints.
Diverse mean-field dynamics of clustered, inhibition-stabilized Hawkes networks via combinatorial threshold-linear networks
Authors: Caitlin Lienkaemper, Gabriel Koch Ocker
Date: 2025-06-06 00:00:00

Networks of interconnected neurons display diverse patterns of collective activity. Relating this collective activity to the network's connectivity structure is a key goal of computational neuroscience. We approach this question for clustered networks, which can form via biologically realistic learning rules and allow for the re-activation of learned patterns. Previous studies of clustered networks have focused on metastabilty between fixed points, leaving open the question of whether clustered spiking networks can display more rich dynamics--and if so, whether these can be predicted from their connectivity. Here, we show that in the limits of large population size and fast inhibition, the combinatorial threshold linear network (CTLN) model is a mean-field theory for inhibition-stabilized nonlinear Hawkes networks with clustered connectivity. The CTLN has a large body of ``graph rules'' relating network structure to dynamics. By applying these, we can predict the dynamic attractors of our clustered spiking networks from the structure of between-cluster connectivity. This allows us to construct networks displaying a diverse array of nonlinear cluster dynamics, including metastable periodic orbits and chaotic attractors. Relaxing the assumption that inhibition is fast, we see that the CTLN model is still able to predict the activity of clustered spiking networks with reasonable inhibitory timescales. For slow enough inhibition, we observe bifurcations between CTLN-like dynamics and global excitatory/inhibitory oscillations.
Functional Architecture of the Human Hypothalamus: Cortical Coupling and Subregional Organization Using 7-Tesla fMRI
Authors: Kent M. Lee, Joshua Rodriguez, Ludger Hartley, Philip A. Kragel, Lorena Chanes, Tor D. Wager, Karen S. Quigley, Lawrence L. Wald, Marta Bianciardi, Lisa Feldman Barrett, Jordan E. Theriault, Ajay B. Satpute
Date: 2025-06-06 00:00:00
Tasks: Functional Connectivity

The hypothalamus plays an important role in the regulation of the bodys metabolic state and behaviors related to survival. Despite its importance however, many questions exist regarding the intrinsic and extrinsic connections of the hypothalamus in humans, especially its relationship with the cortex. As a heterogeneous structure, it is possible that the hypothalamus is composed of different subregions, which have their own distinct relationships with the cortex. Previous work on functional connectivity in the human hypothalamus have either treated it as a unitary structure or relied on methodological approaches that are limited in modeling its intrinsic functional architecture. Here, we used resting state data from ultrahigh field 7 Tesla fMRI and a data driven analytical approach to identify functional subregions of the human hypothalamus. Our approach identified four functional hypothalamic subregions based on intrinsic functional connectivity, which in turn showed distinct patterns of functional connectivity with cortex. Overall, all hypothalamic subregions showed stronger connectivity with a cortical network, Cortical Network 1 composed primarily of frontal, midline, and limbic cortical areas and weaker connectivity with a second cortical network composed largely of posterior sensorimotor regions, Cortical Network 2. Of the hypothalamic subregions, the anterior hypothalamus showed the strongest connection to Cortical Network 1, while a more ventral subregion containing the anterior hypothalamus extending to the tuberal region showed the weakest connectivity. The findings support the use of ultrahigh field, high resolution imaging in providing a more incisive investigation of the human hypothalamus that respects its complex internal structure and extrinsic functional architecture.
Connectome brain fingerprinting: terminology, measures, and target properties
Authors: Matteo Fraschini, Matteo Demuru, Daniele Marinazzo, Luca Didaci
Date: 2025-06-06 00:00:00

Distinguishing one person from another (what biometricians call recognition) is extremely relevant for different aspects of life. Traditional biometric modalities (fingerprint, face, iris, voice) rely on unique, stable features that reliably differentiate individuals. Recently, the term fingerprinting has gained popularity in neuroscience, with a growing number of studies adopting the term to describe various brain based metrics derived from different techniques. However, we think there is a mismatch between its widely accepted meaning in the biometric community and some brain based metrics. Many of these measures do not satisfy the strict definition of a biometric fingerprint that is, a stable trait that uniquely identifies an individual. In this study we discuss some issues that may generate confusion in this context and suggest how to treat the question in the future. In particular, we review how fingerprint is currently used in the neuroscience literature, highlight mismatches with the biometric community definition, and offer clear guidelines for distinguishing genuine biometric fingerprints from exploratory similarity metrics. By clarifying terminology and criteria, we aim to align practices and facilitate communication across fields.