315 papers from ICML 2026, filterable by topic. Each links straight to the original (OpenReview) and its code. Titles and abstracts stay in the authors' words.
Source: BioTender-max/icml2026-ai-bio (CC0-1.0) · updated July 10, 2026 · about the data
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Vincent Guan, Lazar Atanackovic, Kirill Neklyudov
The population dynamics of molecules, cells, and organisms are governed by a number of unknown internal and external forces. In the last decade, population dynamics have…
Danyal Rehman, Charlie B. Tan, Yoshua Bengio, Joey Bose +1
Efficient sampling of molecular systems at thermodynamic equilibrium is a hallmark challenge in statistical physics. This challenge has driven the development of Boltzmann…
Hengyuan Cao, Shizhuo Cheng, Mingxuan Liu, Weicheng Huang +4
The rapid evolution of generative models has unlocked new potentials in protein binder design, a pivotal task in structural biology, by facilitating end-to-end generation via…
Zhitao He, Haolin Yang, Zeyu Qin, Yi R. Fung
While Large Language Models (LLMs) have achieved remarkable success in dyadic (one-on-one) instruction, they face significant challenges in One-to-Many alignment, such as clinical…
Julong Yang, Wen Huang, Junhui Chen, Jian Peng
Recent advances in diffusion models show promise for Structure-Based Drug Design (SBDD), which aims to generate 3D ligand molecules that bind tightly to specific protein targets.…
Arnav Shah, Junzhe Li, Parsa Idehpour, Adibvafa Fallahpour +4
Genomic foundation models have the potential to decode DNA syntax, yet face a fundamental tradeoff. Standard subword tokenizers fragment biologically meaningful motifs such as…
Nicholas Gao, Till Grutschus, Frank Noe, Stephan Günnemann
Neural-network wave functions in Variational Monte Carlo (VMC) have achieved great success in accurately representing both ground and excited states. However, achieving sufficient…
Minghan Li, Fengji Li, Yilin Tao, Yue Deng
Computational protein design typically employs a sequential workflow of structure generation followed by sequence (re)design. While structure generators can be explicitly…
Kieran Didi, Sarah Alamdari, Alex Xijie Lu, Bruce James Wittmann +4
Machine learning methods that predict protein fitness from sequence remain sensitive to changes in data distributions, limiting generalization across common conditions encountered…
Friedrich Hastedt, Dongda Zhang, Antonio Del rio chanona
Current computer-aided synthesis planning (CASP) methods often treat retrosynthesis as solved once a single feasible route is identified, focusing primarily on convergence or…
Tingting Chen, Beibei Lin, Zifeng Yuan, Qiran Zou +4
Many scientific problems are underdetermined: multiple distinct hypotheses are equally consistent with the same observations. In such settings, effective inference requires not…
Amrith Lotlikar, Ian Christopher Tanoh, Praful K. Vasireddy, Andrew Lanpouthakoun +4
Multi-compartment Hodgkin–Huxley (HH) models provide a principled framework for predicting neural dynamics and responses to electrical stimulation. However, fitting HH biophysical…
Winfried Ripken, Michael Plainer, Gregor Lied, Thorben Frank +4
Simulating the long-time evolution of Hamiltonian systems is limited by the small timesteps required for stable numerical integration. To overcome this constraint, we introduce a…
SUBBA REDDY OOTA, Vijay Rowtula, Satya Sai Srinath Namburi GNVV, Khushbu Pahwa +4
Recent work has shown that scaling large language models (LLMs) improves their alignment with human brain activity, yet it remains unclear what drives these gains or which…
Dian Ding, Liren Dong, Yu Lu, Juntao Zhou +4
Gastrointestinal (GI) motility assessment via bowel sounds (BS) offers a non-invasive alternative to resource-intensive clinical standards. However, the diagnostic utility of BS…
Yizhuo Lu, Changde Du, Qingyu Shi, Hang Chen +4
Modeling the interplay between external stimuli and internal neural representations is a pivotal research area for Brain-Computer Interfaces (BCIs). A major limitation of prior…
Haowei Xu, Yixin Chen, Wanyi Fu, Hongbin Han +1
Neuromodulation can be viewed as closed-loop control of high-dimensional spatiotemporal fields on irregular 3D morphologies, coupling membrane electrophysiology with ionic…
Lingyuan Meng, KE LIANG, Hao Li, Meng Liu +4
Multi-modal brain network analysis aims to predict neuropsychiatric status from functional connectomes with heterogeneous phenotypes. However, most existing methods treat…
Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng, Ge Liu +1
We present protein autoregressive modeling (PAR), the first multi-scale autoregressive framework for protein backbone generation via coarse-to-fine next-scale prediction. Using…
Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten Borgwardt
Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoid duplicates and by models…
Chenggang Chen, Zhiyu Yang, Xiaoqin Wang
Deep neural networks currently provide the leading quantitative models of neural responses in sensory systems. However, these networks remain implausible as models of sensory…
Lina Zhang, Tonmoy Monsoor, Peizheng Li, Jiarui Cui +4
While Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in general video understanding, their capacity to interpret involuntary, and…
Zongzhe Xu, Zitao Shuai, Eideen Mozaffari, Ravi Shankar Aysola +2
We present SleepLM, a family of sleep-language foundation models that enable human sleep alignment, interpretation, and interaction with natural language. Despite the critical…
Marie Brockschmidt, Maresa Schröder, Stefan Feuerriegel
Survival analysis is a cornerstone of clinical research by modeling time-to-event outcomes such as metastasis, disease relapse, or patient death. Unlike standard tabular data,…
Hanqun Cao, Aastha Pal, Sophia Tang, Yinuo Zhang +3
Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizing a single conformation. This…
Kexin Zhang, Weichen Qin, Yue Teng, Jiale Yu +4
Agent-centered scientific discovery is turning scientific models into always-on computational infrastructure. In this paradigm, AI agents coordinate tools, interpret feedback, and…
Zipeng Sun, Can Chen, Ye Yuan, Haolun Wu +3
We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem common in robotics, DNA, and materials…
Pingzhi Li, Hongxuan Li, Zirui Liu, Xingcheng Lin +1
Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many-body interactions, but remain…
Aleix Segui Ugalde, Wesley Armour
In AI for Science, physics-informed losses are increasingly used to train learned compressors for scientific data, but their rate--distortion implications remain poorly…
Michael Ibrahim, Hanqi Zhao, Eli Zachary Sennesh, Zhi Li +4
Poisson-distributed latent variable models are widely used in computational neuroscience, but differentiating through discrete stochastic samples remains challenging. Two…
Eric Qu, Brandon M. Wood, Aditi S. Krishnapriyan, Zachary Ward Ulissi
Machine-learning interatomic potentials (MLIPs) have advanced rapidly, with many top models relying on strong physics-based inductive bias. However, as models scale to larger…
Heng Rao, Jason Zipeng Zhang, Yu Gu, Zhenghao Liu +4
Predicting long-horizon trajectories of biological dynamical systems remains challenging due to substantial system heterogeneity. Most existing machine learning approaches are…
Emily Cheng, Aditya R. Vaidya, Richard Antonello
Research has repeatedly demonstrated that intermediate hidden states extracted from large language models and speech audio models predict measured brain response to natural…
Haoyu Albert Wang, Wei P Dai, Jialun Ma, Jiawei Zhang +4
Lateral connections (LCs) are ubiquitous in the cortical circuits. While DL architectures have rich intralayer interactions to support feature selectivity and contextual…
Binghao Liu, Wenzheng Zhao, Zhijie Zheng, Fei Gu
Effective DNA modeling demands the integration of complex patterns such as local motifs, long-range dependencies, and periodic signals. Yet, architectures like CNNs, Transformers,…
Dongxun Jiang, Borui Jia, Yuxuan Wang, Dongdong Zhang
Graph-based attention deficit hyperactivity disorder (ADHD) detection methods have been extensively studied, but comparatively less attention has been paid to short-term brain…
Rituparna Datta, Zihan Guan, Baltazar Espinoza, Yiqi Su +4
Epidemic modeling is essential for public health planning, yet traditional approaches rely on fixed model classes that require manual redesign as pathogens, policies, and scenario…
Guobin Shen, Dongcheng Zhao, Yiting Dong, Qian Zhang +1
Artificial and biological systems may converge on similar computational strategies despite different architectures and learning mechanisms—a form of convergent evolution. We test…
Aryan Pedawi, Jordi Silvestre-Ryan, Bradley Worley, Darren J. Hsu +4
Make-on-demand combinatorial synthesis libraries (CSLs) like Enamine REAL have significantly enabled drug discovery efforts. However, their large size presents a challenge for…
Jun Li, Ziwei Qin
Semi-supervised learning has become a dominant paradigm for reducing annotation costs. However, we argue that the current progress is clouded by a twofold overconfidence problem.…
Luke Nightingale, Joseph Tuersley, Scott Warchal, Andrea Cairoli +4
Phenotypic screening experiments produce many microscope images of cells under diverse perturbations, with biologically significant responses often subtle or difficult to identify…
Yaotian Yang, Yiwen Tang, Yizhe Chen, Xiao Chen +4
Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain similar contrast, and purely…
Silas Ruhrberg Estévez, Christopher Chiu, Mihaela van der Schaar
Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules. While…
Ziyu Zhao, Yiyang Liu, Yajiao Wang, Xiaotao Wang +4
Despite progress of Multimodal Large Language Models (MLLMs) in biomedical visual question answering (VQA), existing benchmarks provide limited assessment of their scientific…
Yuting Yan, Yinghao Fu, Wendi Ren, Haozhou Gao +1
Diagnosing rare diseases remains a persistent challenge, often hindered by cognitive anchoring: once clinicians settle on a common diagnosis, they often discount alternative…
Junda Ying, Yuxuan Wang, Bowen Yang, Peijie Zhou +1
Inferring cellular trajectories from destructive snapshots is complicated by the challenges of stochasticity and non-conservative mass dynamics such as cell proliferation and…
Jiafa Ruan, Ruijie Quan, Xu Liyang, Zongxin Yang +1
Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics. In practice, perturbation responses are rarely gene-independent but…
Tan Pan, Shuhao Mei, Yixuan Sun, Kaiyu Guo +4
Self-supervised pre-training methods in medical imaging typically treat each individual as an isolated instance, learning representations through augmentation-based objectives or…
Maxence Gélard, Hakim Benkirane, Thomas Pierrot, Guillaume Richard +1
Oncologists are increasingly relying on multiple modalities to model the complexity of diseases. Within this landscape, transcriptomic and epigenetic data have proven to be…
Dongyang Ma, Zhengyu Ma, Yifan Huang, Chenlin Zhou +2
Retina-like event cameras and brain-inspired Spiking Neural Networks (SNNs) demonstrate exceptional energy efficiency through bio-inspired sensing and computation. While SNNs are…
Dionizije Fa, Marko Čuljak, Bruno Pandža, Mateo Čupić
We introduce BioAgent Bench, an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks. The suite consists of manually…
Yi Fang, Haoran Xu, Jiaxin Han, Sirui Ding +3
Foundation models have revolutionized AI, yet biological applications often repurpose general architectures without accounting for the intrinsic structural and functional…
Mujie Lin, Yutian Liu, Yudi Guo, Yanzhen Hou +4
Generating long-horizon molecular dynamics (MD) is difficult due to error accumulation in time-domain autoregressive models, which causes drift, and fixed step-size constraints on…
Yuyang Liu, Liuzhenghao Lv, Xiancheng Zhang, Jingya Wang +2
The realization of autonomous scientific experimentation is currently limited by LLMs' struggle to grasp the strict procedural logic and accuracy required by biological protocols.…
Zhuomin Liang, Liang Bai, Xian Yang
scRNA-seq clustering is a critical task for analyzing single-cell RNA sequencing (scRNA-seq) data, as it groups cells with similar gene expression profiles. Transformers, as…
Sunghwan Lee, Jihun Kim, Chae Lynn Kim, Ji Yun Park +1
Decoding fMRI into natural language is challenging because strong, pre-trained language priors can dominate autoregressive generation, obscuring whether a model truly utilizes…
Haitao Wu, Qirui Zhang, Zhouheng Yao, Shangquan Sun +4
Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscience. However, existing…
Guanghui Min, Tianhao Huang, Ke Wan, Qi R. Wang +1
Reliable epidemic forecasting is critical for public health decision-making yet remains challenging due to data sparsity and the non-stationary nature of disease dynamics. While…
Xinhong Xu, Yimeng Zhang, Qichen Qian, Yuanlong Zhang
Recent work suggests that large-scale, multi-animal modeling can significantly improve neural recording analysis. However, for functional calcium traces, existing approaches…
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
Deep protein structure predictors such as AlphaFold provide confidence estimates (e.g., pLDDT) that are not calibrated and degrade under distribution shifts across experimental…
Ziyang Yu, Yi He, Wenbing Huang, Wen Yan +1
Estimating free energy differences quantifies thermodynamic preferences in molecular interactions, which is central to chemistry and drug discovery. Despite fruitful progress,…
Silas Ruhrberg Estévez, Nicolas Huynh, Tennison Liu, Roderik M. Kortlever +3
Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive measurements preclude direct…
Hengrui Lou, Weihan Li, Jiazhen Yang, Lingxiang Jia +4
Computational pathology has made progress in diagnosis and prognosis prediction from whole slide images (WSIs), yet pipelines still rely on patch-level feature extraction and…
Akash Ghosh, Srivarshinee Sridhar, Raghav Kaushik Ravi, Muhsin Muhsin +2
Integrating language models (LMs) in healthcare systems holds great promise for improving medical workflows and decision-making. However, a critical barrier to their global…
Xinrui Chen, Yizhen Luo, Siqi Fan, Zaiqing Nie
*De novo* functional protein design aims to generate protein sequences that realize specified biochemical functions without relying on evolutionary templates, enabling broad…
Weilong Chen, Bojun Zhao, Jan Eckwert, Julija Zavadlav
Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood…
Tianmeng Hu, Biao Luo, Ke Li
Designing RNA sequences that reliably fold into specific secondary structures is essential for understanding their biological functions but remains a challenging computational…
Stephen Zhewen Lu, Aakarsh Vermani, Kohei Sanno, Jiarui Lu +3
Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these…
Sven Gutjahr, Riccardo De Santi, Luca Schaufelberger, Kjell Jorner +1
Adapting generative foundation models, in particular diffusion and flow models, to optimize given reward functions (e.g., binding affinity) while satisfying constraints (e.g.,…
Paul Collart, Juergen Gall, Andrea Schnepf, Holger Pagel +1
Soil microorganisms control organic matter cycling and largely determine how soil systems can cope with and mitigate climate change and environmental threats. Representing…
Lisa Schneckenreiter, Sohvi Luukkonen, Lukas Friedrich, Daniel Kuhn +1
Structure-based and ligand-based computational drug design have traditionally relied on disjoint data sources and modeling assumptions, limiting their joint use at scale. In this…
Zhuoran Li, Xu Sun, Chang Wen Chen, Wanyu Lin
Controllable molecule generation is crucial for diverse scientific applications, such as drug discovery and materials design. While large language models (LLMs) show great…
Luca Thiede, Abdulrahman Aldossary, Andreas Burger, Jorge A. Campos-Gonzalez-Angulo +4
Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions.…
Sihan Wang, Wenjie Du, Yang Wang
In the AI4Chemistry scenario, utilizing heterogeneous data at different fidelity levels is a common and core issue. High-fidelity data is accurate but scarce, while low-fidelity…
Byungwoo Kang, Maceo Richards, Bernardo L. Sabatini
Credit assignment, the process of determining how changes in individual neurons and synapses influence a network’s output, is central to learning in brains and machines. Noise…
Ziyi Yang, Zitong Tian, Yinjun Jia, Tianyi Zhang +4
D-peptide binders targeting L-proteins have promising therapeutic potential. Despite rapid advances in machine learning-based target-conditioned peptide design, generating…
Mohammad Hosseini, Eray Erturk, Saba Hashemi, Maryam M. Shanechi
Large-scale, multi-subject widefield calcium imaging provides unprecedented access to brain-wide cortical dynamics. However, the high dimensionality, complex spatiotemporal…
Chi-Min Chan, Ehsan Hajiramezanali, Xiner Li, Edward De Brouwer +4
In scientific reasoning tasks, the veracity of the reasoning process is as critical as the final outcome. While Process Reward Models (PRMs) offer a solution to the coarse-grained…
Boqiao Lai, Melissa Englund, Ramit Bharanikumar, Isabel Nocedal +3
Modeling recognition between T-cell receptors (TCRs) and peptide-MHC (pMHC) complexes is a fundamental challenge in computational immunology, constrained by sparse paired…
Ivan Svatko, Maxime Sanchez, Ihab Bendidi, Gilles Cottrell +1
Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features. In microscopy imaging, however, it remains…
Yingheng Wang, Tao Yu, Shufeng Kong, Francesco Ricci +2
Structured scientific spectra encode rich physical information while obeying hard constraints, such as conservation laws and characteristic spectral geometry. Accurate prediction…
Xinyu Pang, Zhanke Zhou, Xuan Li, Fangrui Lv +4
Symbolic regression (SR) discovers compact mathematical expressions from data, yet recent LLM-based evolutionary methods remain sample-inefficient because they rely mainly on…
Keyue Qiu, Xintong Wang, Zhilong Zhang, Hao Zhou +1
Biomolecules such as proteins and small-molecule ligands play a central role in biological systems, arising from the tight interplay between sequence and three-dimensional…
Eike Eberhard, Luca Thiede, Abdulrahman Aldossary, Andreas Burger +4
Machine-learned (ML) XC functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still do not consistently…
Shaoqing Duan, Haofei Song, Xintian Mao, Qingli Li +1
Defocus deblurring in pathological microscopy remains challenging due to the spatially varying and locally discontinuous nature of optical blur induced by a position-dependent…
An Zeyu, Wanyu Lin
$\textit{De novo}$ peptide sequencing is a pivotal technique that directly reconstructs amino acid sequences from tandem mass spectrometry (MS/MS) data; it enables the…
Bowen Shi, Weiwei Cao, Ruifeng Yuan, Wanxing Chang +4
Vision–language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet existing methods struggle…
Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sayan Ranu +1
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with…
Changshuo Liu, Wu Junran, Zhongle Xie, Wenqiao Zhang +4
Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside…
Taewon Kim, Jihwan Shin, Hyomin Kim, Youngmok Jung +4
DNA language models are increasingly used to represent genomic sequence, yet their effectiveness depends critically on how raw nucleotides are converted into model inputs. Unlike…
Yucheng Xing, Ling Huang, Jingying Ma, Ruping Hong +4
Whole-slide images (WSIs) are widely used for cancer survival analysis because of their comprehensive histopathological information at both cellular and tissue levels, enabling…
Yuhan Wang, Yuanyuan Zou, Jie Cheng, Bin Dai +1
Accurately capturing local variations in long series has always been one of the most challenging problems in time-series forecasting especially in medical signals, where local…
Ganchao Wei, Daniela F De Albuquerque, Miles Martinez, Shiyang Pan +1
While neuroscience experiments have repeatedly demonstrated the involvement of large populations of neurons in even simple behaviors, these studies have just as often reported…
Jun Li, Mingxuan Liu, Jiazhen Pan, Che Liu +3
Clinical abnormality grounding for rare diseases is often hindered by data scarcity, rendering supervised fine-tuning infeasible and single-pass inference highly unstable. Thus,…
Ezekiel Williams, Alexandre Payeur, Guillaume Lajoie
Biological and neuromorphic recurrent neural networks (RNNs) are subject to spatial and temporal locality constraints on the information that can plausibly be used during…
Jiarui Jin, Haoyu Wang, Xingliang Wu, Xiaocheng Fang +4
Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG…
Runhe Zhou, Shanglin Li, Guanxiang Huang, Xinliang Zhou +4
Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, providing great clinical…
Wei Xiong, Jiangtong Li, Jie Li, Kun Zhu +1
Electroencephalography foundation models (EEG-FMs) have advanced brain signal analysis, but the lack of standardized evaluation benchmarks impedes model comparison and scientific…
McClain Thiel, Angus G. Cunningham, Chris P Barnes
We compare the efficacy and distributional effects of supervised fine-tuning (SFT) and reinforcement learning (RL) post-training for PlasmidGPT, a foundation model for…
Stefan Pranger, Bernhard Ramsauer, Oliver T. Hofmann, Bettina Könighofer
Scanning tunneling microscopy (STM) enables precise manipulation of individual atoms and molecules, offering a pathway to constructing nanoscale assemblies with rich quantum…
Haiyang Yu, Yuchao Lin, Xuan Zhang, Xiaofeng Qian +1
We consider the task of predicting Hamiltonian matrices to accelerate electronic structure calculations, which plays an important role in physics, chemistry, and materials…
Yunyang Li, Lin Huang, Luojia Xia, Wenhe Zhang +1
Generative models for 3D molecular conformations must respect Euclidean symmetries and concentrate probability mass on thermodynamically favorable, mechanically stable structures.…
Ziyu Jia, Junyi Lin, Pu Wan, Jinxin Pi +4
Electroencephalography (EEG) foundation models (EFMs) have achieved strong performance across a wide range of downstream EEG tasks via pretraining and fine-tuning. Through…
Zaifei YANG, Samuel Ping-Man Choi, James Kwok
Protein-protein interactions (PPIs) are essential for many biological processes. However, existing PPI prediction approaches suffer from two major limitations: they overlook the…
Haoyang Luan, Gufeng Yu, Letian Chen, Zhenran Xiao +3
The *de novo* generation of high-affinity epitopes tailored to specific major histocompatibility complex (MHC) proteins is a pivotal challenge in computational immunotherapy.…
Jingbo Yang, Yunfeng Zhao, Chao Qiu, Yulin Sun +2
Stereotactic electroencephalography (sEEG) provides temporally precise intracranial recordings but is inherently constrained by sparse and irregular spatial sampling due to…
Chenyu Lian, Hong-Yu Zhou, Jing Qin
Disease screening is critical for early detection and timely intervention in clinical practice. However, most current screening models for medical images suffer from limited…
Yaowei Jin, Junjie Wang, Cheng Cao, Penglei Wang +2
Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean and probabilistic spaces for…
Dongkyu Cho, Miao Zhang, Gregory D Lyng, Rumi Chunara
Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. While large language models…
Jeonghyeon Kim, Nathaniel Blalock, Ameya Kulkarni, Kensuke Nakamura +1
Antibodies originate from germline templates and are diversified by somatic hypermutation, producing sequences in which conserved germline residues scaffold structure while rare…
Pengfei Hu, Chang Lu, Feifan Liu, Yue Ning
Deep learning models for clinical event prediction on electronic health records (EHR) often suffer performance degradation when deployed under different data distributions. While…
Yingyu Chen, Yongqiang Huang, Yang Qin, Ziyuan Yang +3
In clinical practice, patients often present with multiple co-occurring diseases, yet most existing Multi-Label-Diagnosis (MLD) methods treat diagnosis as a rigid discriminative…
Luru Jing, Cong Cong, Yanyuan Chen, Yongzhi Cao
Federated learning (FL) offers a promising framework for collaborative digital pathology by enabling model training across institutions. However, real-world deployments face…
Michela Proietti, Roberto Capobianco, Mariya Toneva
Understanding the alignment between large language models (LLMs) and human brain activity can reveal computational principles underlying language processing. This work describes a…
Qi Si, Penglei Wang, Yushuai Wu, Yifeng Jiao +4
Predicting spatial gene expression from routine H\&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks, thereby overlooking…
Martin Jankowiak, Yerdos Ordabayev, Rudraksh Tuwani, Henry Neil Ward +3
Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experimental data remains a…
Riccardo Tedoldi, Ola Engkvist, Patrick Bryant, Hossein Azizpour +2
Sampling useful three-dimensional molecular structures along with their most favorable conformations is a key challenge in drug discovery. Current state-of-the-art 3D de-novo…
Egor Antipov, Alessandro Palma, Lorenzo Consoli, Stephan Günnemann +2
Estimating density ratios between pairs of intractable data distributions is a core problem in probabilistic modeling, enabling principled comparisons of sample likelihoods under…
Yinbo Liu, Keyang Ye, Wenshan Sun, Handi Gao +1
Reconstructing unified continuous dynamics from sparse, non-contiguous, and unpaired point cloud snapshots remains a fundamental challenge in spatiotemporal analysis for computer…
Zheng Zhang, Hao Tang, Yingying Hu, zhanli hu +1
Low-count Positron Emission Tomography (PET) reconstruction is severely hindered by the dissipative nature of prevailing generative models, where the inherent phase-space…
Qi Chen, Shuhan Ding, Yu Gu, Nan Liu +4
Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, training CT-specific VAEs from…
Nicholas Blauch, George A. Alvarez, Talia Konkle
Human vision is foveated, with variable resolution peaking at the center of a large field of view; this reflects an efficient trade-off for active sensing, allowing eye-movements…
Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer +3
Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging.…
Wenhao Wu, Zhentao Tang, Yafu Li, Shixiong Kai +4
Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical…
Ryan Liu, Eric Qu, Tobias Kreiman, Samuel M Blau +1
Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in…
Jiahao Chen, Letian Gao, Yanhao Zhu, Wenbiao zhou +3
Recent advances in generative modeling have enabled significant progress in structure-based drug design (SBDD). Existing methods typically condition molecule generation on empty…
Yongqi Jin, Yecheng Wang, Jun-jie Wang, Rong Zhu +2
Accurate prediction of nuclear magnetic resonance (NMR) chemical shifts is fundamental to spectral analysis and molecular structure elucidation, yet existing machine learning…
Zhaokun Yan, Shan Xu, Wuzheng Dong, Zhaohan Liu +4
Public health reasoning requires population level inference grounded in scientific evidence, expert consensus, and safety constraints. However, it remains underexplored as a…
Junfeng Zuo, Yuhang He, Wenhao Zhang, Fang Fang +1
Navigation in complex environments relies on internal spatial representations that guide action. While the brain employs a diverse repertoire of spatial tuning cells—including…
Mingcheng Zhu, Zhiyao Luo, Yu Liu, Tingting Zhu
By processing electronic health records (EHRs) as natural language sequences, large language models (LLMs) have shown potential in clinical prediction tasks such as mortality…
Jiaqi Men, Hua Liu, Yiming Tang, Jinhong You +2
Accurate survival prediction in kidney transplantation is critical yet challenging due to the complex interplay between functional biomarkers and patient characteristics under…
Noah Flynn
Designing genetic circuits, which are biological systems capable of programmed behaviors within living cells, remains a laborious, expert-driven process despite decades of…
Daria Ledneva, Mikhail Nuridinov, Denis Kuznetsov
Progress in genomic foundation models is difficult to assess due to fragmented benchmarks, incompatible evaluation protocols, and task-specific reporting. As a result, claims of…
Yiyuan Zhang, Cailong Hua, VINITENDRA SINGH, Joseph M. Muretta +2
Many fundamental biological processes are governed by mechanical forces, with proteins acting as the key molecular mediators. Elucidating how protein unfolding responds to force…
Sung Moon Ko, Jaewan Lee, Sumin Lee, Soorin Yim +1
Geometrical interpretations of deep learning models offer insightful perspectives into their underlying mathematical structures. In this work, we introduce a novel approach that…
Haoran liu, Xiaoli Lin, Jing Hu, Yu Zou +1
Polypharmacology provides a powerful strategy for treating complex diseases, but identifying molecules that simultaneously satisfy coupled constraints across multiple biological…
Jialin Li, Zhuo Zhang, Cao Yue, Guipeng Lan +3
The scarcity of high-quality imaging data for coronary angiography (CAG) stenosis limits the clinical translation of automated stenosis detection. Synthetic stenosis data provides…
Subati Abulikemu, Tiago Azevedo, Michail Mamalakis, John Suckling
In network neuroscience, functional brain systems are often characterized using separate yet related graph-theoretic or spectral descriptors, overlooking how these properties…
Parth Verma, Parv Pratap Singh, Vipul Garg, Ishita Thakre +2
Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new…
Yichi Zhang, Nabeel Seedat, Yinpeng Dong, Peng Cui +2
As LLM-powered agents have been used for high-stakes decision-making, such as clinical diagnosis, it becomes critical to develop reliable verification of their decisions to…
Wei Wang, Fang He, Yifan Li, Wanying Qu +3
Existing EEG models are limited by electrode heterogeneity and rigid "channel-first" architectures that treat sensors as independent features. We propose Brain Signal Rendering…
Zelin Zang, WenZhe Li, Yongjie Xu, Chang Yu +4
In single-cell research, tracing and analyzing high-throughput single-cell differentiation trajectories is crucial for understanding biological processes. Key to this is the…
Sirui Li, Shuhan Xiao, Mihir Joshi, Ahmed Metwally +3
The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of…
Keunho Byeon, Jin Tae Kwak
Spatial transcriptomics offers spatially resolved gene expression profiling within tissue sections, but its cost and limited throughput hinder large-scale deployment. To extend…
Yifan Lin, Kevin Z. Lin
High-dimensional genomics studies are frequently confounded by unmeasured biological processes that obscure disease-specific signals. While existing workflows can estimate these…
Weiyi Wu, Xinwen Xu, Xingjian Diao, Siting Li +3
Spatial transcriptomics (ST) links gene expression with tissue morphology but remains expensive and low-throughput, motivating surrogates that infer expression from routine…
Yiqi Su, Ray Lee, Jiaming Cui, Naren Ramakrishnan
Epidemiological forecasting from surveillance data is a hard problem and hybridizing mechanistic compartmental models with neural models is a natural direction. The mechanistic…
Shuqi Zhu, Yi Zhong, Ziyi Ye, Bangde Du +3
While AI-generated hallucinations pose considerable risks, the underlying cognitive mechanisms by which humans can successfully recognize or be misled by these hallucinations…
Maria Elkjær Montgomery, Christian Igel, Mikkel Fruelund Odgaard, Martin Sillesen +1
How do we encode numeric values in transformer-based sequence processing, particularly in electronic health record (EHR) data? We systematically compare discrete, continuous, and…
Shuo Tang, Jiadong Zhang, Gengxian Zhou, Qizhao Jin +4
While deep learning-based weather forecasting paradigms have made significant strides, addressing extreme weather diagnostics remains a formidable challenge. This gap exists…
Timothy Doyeon Kim, Ulises Pereira Obilinovic, Yiliu Wang, Eric Todd SheaBrown +1
Connectivity structure shapes neural computation, but inferring this structure from population recordings is degenerate: multiple connectivity structures can generate identical…
Yang Song, Yixuan Zhang, Lingfa Meng, Tongyuan Hu +4
Parameter-efficient adaptation has made LLMs practical for domain prediction, but standard LoRA still relies on a static low-rank update and does not expose the latent…
Ganxi Xu, Zhao-Rong Lai, Yuting Tang, Yonghao Song +4
Visual prostheses hold great promise for restoring vision in blind individuals. While researchers have successfully utilized M/EEG signals to evoke visual perceptions during the…
Xiang Fang, Wanlong Fang, Wei Ji
Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. However, this semantic…
Sai Advaith Maddipatla, Anar Rzayev, Marco Pegoraro, Martin Pacesa +4
Protein function relies on dynamic conformational ensembles, yet current generative models like AlphaFold3 (AF3) often fail to produce ensembles that match experimental data.…
Pengkai Wang, Pengwei Liu, Qi Zuo, Zhijie Sang +2
Reinforcement learning (RL) has powered many recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be computed automatically, such as code…
Evgeny Saveliev, Samuel Holt, Nabeel Seedat, David L. Bentley +2
Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by inefficient search strategies…
Cheng Wang, Jinpu Cai, Chongxiao Mao, Yuxuan Wang +4
Geometry-preserving dimension reduction is critical for single-cell transcriptomics, where low-dimensional distances should reflect biological divergence between cell types along…
Akash Pandey, Wei Chen, Sinan Keten
Designing biological sequences such as proteins and DNA for desired properties is challenging due to vast search spaces and limited wet lab evaluation budgets. Current…
Zaikang Lin, Sei Chang, Aaron Zweig, Minseo Kang +3
Modern high-throughput biological datasets containing thousands of perturbations enable large-scale discovery of causal graphs that represent regulatory interactions between…
Akira A Nair, Jaehyun Joo, Jonghyun Lee, Lina Takemaru +4
Genomic language models (gLMs) achieve strong performance across genomic prediction tasks, but their internal biological representations remain poorly understood. Sparse…
Hongyu Wang, Weijian Liu, Hongtao Xu, Yan Wang +3
Discovering atom-level phenomena requires molecular dynamics (MD) simulations with ab initio accuracy. Machine learning interatomic potentials (MLIPs) enable stable, high-accuracy…
Haoning Wang, Wenchao Yang, Shuai Shen, Yang Li
While EEG foundation models have shown significant potential in universal neural decoding across tasks, their advancement remains constrained by the inadequacy modeling of…
Haokai Hong, Wanyu Lin, KC Tan
Large-scale molecular dynamics simulations are essential in understanding chemical and biological processes, necessitating the accurate and efficient modeling of interatomic…
Daria Ledneva, Denis Kuznetsov
Genomic foundation models increasingly adopt large language model architectures, yet almost universally rely on fixed tokenization schemes such as $k$-mers, BPE, or single…
Hwanhee Kim, Seungyeon Choi, Sanghyun Park
Generative Flow Networks (GFlowNets) have emerged as a powerful framework for molecular generation, sampling diverse candidates proportionally to a reward function. However, the…
Yinhua Piao, Hyomin Kim, Seonghwan Kim, Yunhak Oh +4
Predicting high-dimensional transcriptional responses to genetic perturbations is challenging because signals are sparse and experimental noise is severe. Existing methods often…
Zixun Zhang, Yuncheng Jiang, Yuzhe Zhou, Jiayou Zheng +2
Tertiary structure-based RNA design aims to generate RNA sequences that can fold into desired 3D structures, but remains a challenging problem due to the scarcity of annotated…
Robin Winter, Julian Cremer, Djork-Arné Clevert
Virtual screening of billion-scale molecular libraries based on 3D shape similarity remains computationally prohibitive, requiring expensive conformational sampling and alignment,…
Christian Nauck, Junyou Zhu, Michael Lindner, Frank Hellmann
The robustness of synchronization is typically characterized by scalar, per-node stability indices whose dependence on topology is studied via network science or graph neural…
Huayu Li, ZhengXiao He, Xiwen Chen, Jingjing Wang +3
Learning meaningful representations from medical time series (MedTS), such as ECG or EEG signals, is a critical challenge. These signals are often high-dimensional,…
Jaeyoon Sim, Soojin Hwang, Seunghun Baek, Guorong Wu +1
Understanding complex interactions between brain regions is critical for early neurodegenerative disease classification such as Alzheimer’s Disease (AD) and Parkinson’s Disease…
Mingqing Wang, Zhiwei Nie, ATHANASIOS V. VASILAKOS, Yonghong He +1
Proteins encode diverse functions within complex three-dimensional structures, yet most deep learning representations remain highly entangled, obscuring the biophysical signals…
Ziqi Gao, Chenyi Zi, Zijing Liu, Ziqiao Meng +2
Protein-protein interactions (PPIs) are fundamental to cellular function, disease mechanisms, and drug discovery. Current learning-based PPI predictors focus on learning powerful…
Sofia Ek, Dave Zachariah
Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. For such allocations, a certain proportion of treated patients…
Yicheng Feng, Hairong Chen, Ziyu Jia, Samir Bhatt +1
Alzheimer’s disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagnosis increasingly important…
Yifan Wang, Yijia Ma, Wen Li, Chenyu You
High-fidelity EEG generation is critical for alleviating data scarcity and addressing privacy constraints in large-scale neural modeling. Despite recent progress, most existing…
Shizhao Joshua Yang, Yixin Wang, Kevin Z. Lin
Deciphering how cells commit to future fates is essential for developing precision therapeutics that can reprogram stem cells or modulate immune functions. However, isolating…
Yuxing Wang, Wenyi Zhang, Yilong Zou, Jing Huang
Computational identification of lipid-binding proteins is critical for both fundamental research and therapeutic development. Existing models are typically trained in a fully…
Huimin Yan, Liang Bai, Xian Yang, Long Chen
Most existing CLIP-style medical vision--language pretraining methods rely on global or local alignment with substantial paired data. However, global alignment is easily dominated…
Yihang Liu, Longzhen Yang, Jiaxiong Yang, Ying Wen +2
Medical foundation models (MFMs) aim to learn universal representations from multimodal medical images that can generalize effectively to diverse downstream clinical tasks.…
Alicja Maksymiuk, Alexandre Duplessis, Michael M. Bronstein, Alexander Tong +2
Macrocycles are ring-shaped molecules that offer a promising alternative to small-molecule drugs due to their enhanced selectivity and binding affinity against difficult targets.…
Ziquan Wei, Tingting Dan, Guorong Wu
Despite the central role of sensor-derived measurements such as imaging traits and plasma biomarkers in biomedical research and clinical practice, existing generative models for…
Chenghao Jia, Mengdi Liu, Hong Chang, Shiguang Shan +1
Elucidating molecular structures from spectra is a foundational problem in chemical and materials characterization, yet remains challenging due to spectral ambiguity and the vast…
Anglin Liu, Ruichao Chen, Yi Lu, Hongxia Xu +1
Despite recent Multimodal Large Language Models (MLLMs)' linguistic prowess in medical diagnosis, we find even state-of-the-art MLLMs suffer from a critical perceptual deficit:…
Salma J. Ahmed, Emad Mohammed, Azam Asilian Bidgoli
Modern segmentation models achieve strong predictive performance but remain largely opaque, limiting our ability to diagnose failures, understand dataset shift, or intervene in a…
Tianbo Wang, Yuqing Ma, Lingyan Meng, Zhange Zhang +4
Medical Large Vision-Language Models (Med-LVLMs) suffer from severe hallucinations, posing critical safety risks in clinical deployment. Editing LVLM activations has shown promise…
Yu Zhao, Hao Guan, Yongcheng Jing, Ying Zhang +1
Large Language Models (LLMs) have shown strong potential in complex medical reasoning yet face diminishing gains under inference scaling laws. While existing studies augment LLMs…
Ziyuan Gao
Medical image segmentation faces a fundamental challenge in continual learning: data arrives sequentially from heterogeneous sources, yet effective continual learning requires…
Harshit Rajgarhia, Shuubham Ojha, Asif Shaik, Akhil Pothanapalli +3
Medical audio data is difficult to collect due to privacy regulations and high annotation costs arising from domain expertise. Thus, existing benchmarks tend to underrepresent…
Shujun Xia, Haokun Lin, Yichen Wu, Yinan Zhou +4
Large Language Models (LLMs) hold great promise for healthcare applications, but fast-changing medical knowledge can quickly make their outputs outdated or inaccurate, limiting…
Wenjie Li, Yujie Zhang, Haoran Sun, Xingqi He +4
Long-form clinical videos are central to visual evidence-based decision-making, with growing importance for applications such as surgical robotics and related settings. However,…
Aofei Chang, Le Huang, Alex James Boyd, Parminder Bhatia +3
Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision–language comprehension and medical image segmentation. However, existing…
Dulhan Jayalath, Oiwi Parker Jones
Clinical brain-to-text interfaces are designed for paralysed patients who cannot provide extensive training recordings. Pre-training improves data-efficient generalisation by…
Yamin Li, Shiyu Wang, Chang Li, Ange Lou +4
Functional magnetic resonance imaging (fMRI) provides dynamic measurements of human brain activity at high spatial resolution and depth, but its use is constrained by high cost,…
Yichen Luo, Peiyu Zhu, Dongxiao Hu, Jia Wang +4
While Physics-Informed Neural Networks (PINNs) are powerful for solving Partial Differential Equations (PDEs), their training is often paralyzed by gradient pathology. The…
Qiang Zhou, Hanzhen Zhu, Pan Wang, Rui Tu +4
According to the reformulated Learned Helplessness theory, repeated exposure to uncontrollable negative events can foster a depressogenic attributional style—increasing…
Boqiang Xu, Wei Zhang, Ding Ma, Jian Liang +2
Operating room (OR) scene graph generation (SGG) enables holistic modeling of OR domains by encoding interactions among medical staff, tools, and equipment as triplet-based…
Mehmet Yigit Balik, Harri Lähdesmäki
Single-cell RNA sequencing provides insights into gene expression at single-cell resolution, yet inferring temporal processes from these static snapshot measurements remains a…
Zhiran Hou, Tinghuai Ma, Huan Rong, Li Jia +3
Predicting molecular properties from three-dimensional structures is fundamentally hindered by limited labeled data. While researchers have adapted self-supervised pre-training…
Zitao Chen, Jiatong Ji, Yinjun Jia, Wei-Ying Ma +1
Recent advances in 3D molecular modeling have achieved high-fidelity structural synthesis, yet these models often lack an explicit and manipulable representation space. To address…
Susu Hu, Stefanie Speidel
Inferring spatial transcriptomics (ST) from histology enables scalable histogenomic profiling, yet current methods are largely restricted to single-tissue models. This…
Nassim Oufattole, Matthew B.A. McDermott, Collin Stultz
Autoregressive generative models for irregularly sampled clinical time-series data are increasingly used for zero-shot risk forecasting. Prior work typically adopts a single…
Yitian Wang, Fanmeng Wang, Angxiao Yue, Wentao Guo +2
Modeling peptide cyclization is critical for the virtual screening of candidate peptides with desirable physical and pharmaceutical properties. This task is challenging because a…
Noga Mudrik, Yuxi Chen, Gal Mishne, Adam Shabti Charles
Many fields collect large-scale temporal data through repeated measurements (`trials’), where each trial is labeled with a set of metadata variables spanning several categories.…
Abdulkadir Gokce, Yingtian Tang, Martin Schrimpf
Task-optimized neural networks are the leading in-silico models of sensory cortex, yet the field lacks a unified understanding of which modeling choices drive improved brain…
Ruihan Guo, Chaoran Cheng, Zhanghan Ni, Neil He +2
Protein mutation effect prediction is fundamental to protein engineering and disease variant interpretation, yet experimentally measured mutation data remain accurate but…
Itai Zilberstein, Ioannis Anagnostides, Zachary W. Sollie, Arman Kilic +1
Online matching has been a mainstay in domains such as Internet advertising and organ allocation, but practical algorithms often lack strong theoretical guarantees. We take an…
Gabriel Mateo Mejia, Henry E Miller, Francis J.A. Leblanc, BO WANG +2
Recent benchmarks reveal that single-cell perturbation response models are often outperformed by simply predicting the dataset mean. Through large-scale *in silico* simulations,…
Annabel Large, Ian Holmes
Most statistical phylogenetics analyses use simple continuous-time finite-state Markov models of point substitution to describe molecular evolution. These models enforce…
Sijin Yu, Zijiao Chen, Zhenyu Yang, Zihao Tan +4
Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically faithful surface-based models. We argue this is partly driven by…
Hui Zheng, Haiteng Wang
Foundation models for intracranial neural recordings aim to learn generalizable representations from large-scale unlabeled data. However, existing approaches rely on suboptimal…
Marta Hasny, Laura Alexandra Daza, Keno Bressem, Maxime Di Folco +1
Large-scale medical biobanks provide imaging data complemented by extensive tabular information, such as clinical measurements or demographics. However, this abundance of tabular…
Mo Wang, Wenhao Ye, Junfeng Xia, Junxiang Zhang +4
Self-supervised fMRI foundation models have shown promising transfer performance, yet most rely on predefined region-level parcellations that discard fine-grained voxel…
Chunlei Li, Zixuan Zheng, Yilei Shi, Guanglu Dong +4
Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labels. We address this challenge…
Francesco Innocenti, El Mehdi Achour, Rafal Bogacz
Predictive coding (PC) is a biologically plausible alternative to standard backpropagation (BP) that minimises an energy function with respect to network activities before…
Jiaming Zhuo, Shuai Zhai, Ziyi Ma, Kun Fu +4
Brain network classification is pivotal for diagnosing neurological disorders, yet identifying interpretable functional biomarkers fundamentally relies on precise parcellation.…
Yankai Jiang, Yujie Zhang, Peng Zhang, Wenjie Li +4
Recent medical MLLMs have made significant progress in generating step by step textual reasoning chains. However, they still struggle with complex clinical tasks that necessitate…
Shaohao Rui, Kaitao Chen, Weijie Ma, Xiaosong Wang
Extended Chain-of-Thought (CoT) reasoning has significantly bolstered the capabilities of medical large language models (LLMs). However, current models exhibit static…
Jiangtao Yan, Yanlin Qu, Yansheng Qiu, Shujian Gao +4
The longitudinal management of blinding fundus diseases constitutes a Partially Observable Markov Decision Process (POMDP) necessitating a critical precision-risk trade-off…
Chenguang Wang, Zihan Zhou, LEI BAI, Tianshu Yu
Template-free retrosynthesis methods treat the task as black-box sequence generation, limiting learning efficiency, while semi-template approaches rely on rigid reaction libraries…
Zitao Shuai, Zongzhe Xu, David Yang, Wei Wang +1
Polysomnography (PSG) provides the gold standard for sleep assessment but suffers from substantial heterogeneity across recording devices and cohorts. There have been growing…
KIEREN YU, Ziyang LIU, Chang Huang, Kaishun Wu
EEG foundation models aim to learn transferable representations, yet EEG recordings are dominated by high-frequency noise and large cross-subject variability. Existing pretraining…
Zhengqiu Yu, Yueping Ding, Xiangrong Liu
Patient-level sepsis prediction requires models that track clinical deterioration over time and integrate heterogeneous structured evidence from electronic health records. We…
Xiran Chen, Xiaoke Yang, Jian Zhou, Zhao Lv +1
Auditory attention decoding (AAD) based on Electroencephalography (EEG) aims to identify the attended speaker in multi-speaker environments. However, existing methods typically…
Song Ouyang, Zhijie Dong, Yong Luo, Kehua Su +3
Computational protein design holds immense promise across diverse domains, but existing approaches face significant challenges: traditional physics-based methods require…
Jiahao Kuang, Nuowei Liu, Changzhi Sun, Jie Wang +2
Function-guided protein design is a crucial task with significant applications in drug discovery and enzyme engineering. However, the field lacks a unified and comprehensive…
Marcin Możejko, Adam Bielecki, Jurand Prądzyński, Hyun-Su Lee +4
Antimicrobial peptide discovery is challenged by the astronomical size of peptide space and the relative scarcity of active peptides. While generative models provide latent maps…
Xinyu Yuan, Xixian Liu, Ya Shi Zhang, Zuobai Zhang +2
Building _Virtual Cells_ that can accurately simulate cellular responses to perturbations is a long-standing goal in systems biology. A fundamental challenge is that…
Zhixiang Cheng, Hongxin Xiang, Mingquan Liu, Tengfei Ma +4
Precise property prediction of organic materials is pivotal for next-generation electronic and energy devices. In density functional theory (DFT), the electron density (ED) serves…
Kaiwen Zha, Chao Li, Hao He, Peng Cao +4
This paper introduces a novel cross-physiology translation task: synthesizing sleep electroencephalography (EEG) from respiration signals. To address the significant complexity…
Hao Zhou, Simon A. Lee, Cyrus Tanade, Keum San Chun +4
Biosignals acquired from different locations on the body often provide temporally ordered views of the same underlying physiological process. However, most existing…
Chenhe Du, Xuanyu Tian, Qing Wu, Muyu Liu +3
Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained generative models as modular…
Maria Emilia Russo, Federico Di Valerio, Alessia Borghini, Alessio Ragno +1
Computational approaches have become central to Protein–Protein Interaction (PPI) research, complementing experimental techniques that remain costly and incomplete. While modern…
Micaela Elisa Consens, Kevin K Yang, James Brian Hall, Ashley Mae Conard +4
Genome language models (gLM) have the potential to further understanding of regulatory genomics without requiring labeled data. Most gLMs are pretrained using sequence…
Josh Qixuan Sun, Morteza Babaie, Wenyang hou, Mark Crowley +1
Antibody expression ranking is a critical task in antibody design, yet its modelling is severely hindered by the scarcity of labeled experimental data. To address this, we propose…
Jiwoong Sohn, Tomasz Sternal, Kenneth Styppa, Torsten Hoefler +1
Reasoning in knowledge-intensive domains remains challenging as intermediate steps are often not locally verifiable: unlike math or code, evaluating step correctness may require…
Bonjae Ku, Seeun Kim, Yubeen Kim, Hahnbeom Park +1
Proteins are inherently dynamic, with biological functions often emerging from transitions between multiple conformational states. While recent breakthroughs have largely…
Cong Liu, Milong Ren, Jiaqi Guan, Chengyue Gong +3
Recent advances in $\textit{de novo}$ protein binder design have enabled increasing experimental validation, yet reported $\textit{in silico}$ metrics remain difficult to…
Darin Tsui, Kunal Talreja, Daniel Saeedi, Amirali Aghazadeh
Protein language models (pLMs) have emerged as powerful predictors of protein structure and function. However, the computational circuits underlying their predictions remain…
Jacopo Teneggi, SM Bargeen Alam Turzo, Tanya Marwah, Alberto Bietti +3
Large language models (LLMs) are capable of emulating reasoning and using tools, creating opportunities for autonomous agents that execute complex scientific tasks. Protein design…
Panagiotis Antoniadis, Beatrice Pavesi, Simon Olsson, Ole Winther
Molecular dynamics (MD) is a central computational tool in physics, chemistry, and biology, enabling quantitative prediction of experimental observables as expectations over…
Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao +4
Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synthesize molecular geometries…
Jonathan Dan, Amirhossein Shahbazinia, Christodoulos Kechris, David Atienza
Reliable automatic seizure detection from long-term electroencephalogram recordings (EEG) remains an unsolved challenge, as current models often fail to generalize across patients…
ZIYU XU, zijian zhang, Liang Wang, Zhiyuan Liu +3
When reliable target structures are unavailable at scale or phenotypes arise from dysregulated pathways, transcriptomic perturbations provide a system-level functional readout for…
Zeyu Wang, Xin Zheng, Yao Lu, Shanqing Yu +2
Few-shot molecular property prediction (FSMPP) is essential in drug discovery and materials design, where high-quality labeled data are often scarce and expensive to obtain.…
Jinhan Liu, Mahsa Shoaran
Affective and cognitive disorders manifest as distributed, time-varying brain network dynamics across regions, channels, and time, challenging robust representation learning from…
Haonan Zhang, Qing Wu, Xuanyu Tian, Bowen Li +2
Implicit Neural Representation (INR) has emerged as a powerful paradigm for continuous MRI reconstruction. However, standard self-supervised INR requires time-consuming…
Tianwei Lin, Zhongwei Qiu, Jie Cao, Jiang Liu +4
Medical vision-language models (VLMs) have rapidly advanced as general-purpose multimodal assistants, yet their deployment in 3D Computed Tomography (CT) analysis remains…
Marius Knorr, Robert Müller, Jan Peter Bremer, Nils Schweingruber
Fast Healthcare Interoperability Resources (FHIR) is the dominant standard for interoperable exchange of healthcare data. In FHIR, electronic health records form a directed graph…
Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le +3
Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference. Expert…
Yipan Wei, Wenke Huang, Yapeng Li, He Li +3
Federated Prompt Learning (FPL) adapts Vision-Language Models to privacy-sensitive medical imaging, typically via a textual tuning paradigm that assumes the frozen visual encoder…
Hongxin Xiang, Pengsen Ma, Yunkang Cao, Di Yu +3
Recent genomic foundation models largely adopt large language model architectures that treat DNA as a one-dimensional token sequence. However, exhaustive sequential reading is…
Xinyi Li, Sai Wang, Yutian Lin, Yu Wu
Retrosynthesis prediction aims to infer the reactant molecules based on a given product molecule, which is a fundamental task in chemical synthesis. However, existing methods rely…
Liao Chang, Luotian Yuan, Yiping Ke, Ying Wei
Multi-step retrosynthesis planning is a fundamental challenge in organic chemistry, defined by its enormous search space. Existing methods typically formulate it as a Markov…
Zhou Zhang, Hanqun Cao, Cheng Tan, Fang Wu +2
Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures…
Yaxuan Song, Jianan Fan, Tianyi Wang, Qiuyue Hu +3
Histopathology whole-slide images (WSIs) are routinely acquired in clinical practice and contain rich tissue morphology but lack direct molecular architecture and functional…
Haorui Sun, Ardyn Vivienne Olszko, Niharika Singh, David C. Jangraw
We present Reading Observed At Mindless Moments (ROAMM), a multimodal dataset comprising 50 hours of simultaneous EEG and eye-tracking recordings collected during naturalistic…
Shuohao Gao, Xuanzhong Chen, Lingxiao Luo, Zilin Ding +3
Diagnosing complex diseases is inherently a sequential and iterative medical investigation process, in which a clinician strategically requests multiple rounds of diagnostic tests…
Giovanni Palla, Sudarshan Babu, Payam Dibaeinia, James D Pearce +4
Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression profiles remains a major challenge. This…
Wenhan Gao, Jingxiang Qu, Yi Liu
Diffusion models typically operate in fixed-dimensional metric spaces, whereas 3D geometric molecular data vary in dimensionality because molecules differ in size (number of…
Connor Lane, Mihir Tripathy, Leema Krishna Murali, Ratna Sagari Grandhi +4
We study the problem of training self-supervised foundation models for functional MRI. Our main contributions are: (1) we introduce a new model family (CortexMAE) trained using…
Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer, Doron Haviv +4
Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single-cell RNA sequencing enables characterization…
Jiawei Zhang, Kangjun Jin, Shuai Xiao, Jiachen Yang
Measuring single-cell protein abundance is essential for resolving biological mechanisms and disease progression with high resolution. However, due to the high costs and antibody…
Davide D'Ascenzo, Sebastiano Cultrera di Montesano
Training deep learning models on single-cell datasets with hundreds of millions of cells requires loading data from disk, as these datasets exceed available memory. While random…
Jieun Sung, Wankyu Kim
Single-cell foundation models trained on millions of cells can learn gene expression patterns across diverse contexts. However, for predicting genetic perturbation effects they…
Mingxuan Wang, Gaoyang Jiang, ZiJia Ren, Cheng Chen +3
Single-cell RNA-seq profiles are high-dimensional, sparse, and unordered, causing autoregressive generation to impose an artificial ordering bias and suffer from error…
Jingjing Hu, Dan Guo, Haofan Cheng, Zeng ying +3
Despite the high accuracy of EEG-based emotion recognition, existing models remain opaque "black boxes", lacking semantic grounding between abstract neural features and…
Jian Chen, Yipeng Du, Wenhao Yuan, Shuai Wang +4
Electrocardiogram (ECG) representation learning via ECG-report alignment is often hindered by the inherent structural and statistical divergence between signals and natural…
Jiayu Lu, Yujin Wang, Xiaofeng Liu, Dandan Li +1
Functional brain network analysis plays an important role in understanding and diagnosing psychiatric disorders. However, current methods struggle with subject variations,…
Yuqiao Meng, Luoxi Tang, Dazheng Zhang, Rafael Brens +4
The rapid adoption of large language models (LLMs) in digital health has been driven by a "scaling-first" philosophy, i.e., the assumption that clinical intelligence increases…
Kevin Han, Haolin Cong, Bowen Deng, Amir Barati Farimani
Machine learning interatomic potentials (MLIPs) have proven to be wildly useful for molecular dynamics simulations, powering countless drug and materials discovery applications.…
YuCheng Yuan, Yuanfeng Ji, Zhongxiao Li, Ruijiang Li
Spatial proteomics enables single-cell-resolution characterization of protein expression within tissue architecture, playing a critical role in understanding tumor…
Zekuan Shang, Xiaosong Han, Liupu Wang, Wei Du +4
For denoising Spatially Resolved Transcriptomics (SRT) data, existing methods often construct spot and gene graphs to model inter-spot and inter-gene relationships, respectively.…
Zhenglun Kong, Mufan Qiu, John Boesen, xiang lin +4
Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics…
Arthur Kosmala, Stephan Günnemann, Meng Gao, Brandon M. Wood
Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficult to increase single-system…
Ying Wang, Guoheng Huang, Chan-Tong Lam, Xiaochen Yuan
Reliable medical audio diagnosis requires models that are both accurate and honest about their uncertainty. However, fine-tuned models on small, imbalanced datasets often become…
Yongzhi She, Qihua Zhou, Yuhao Wang, Yaodong Huang +2
Recently, the spiking neural networks (SNNs) have shown great promise in enhancing AI task performance by utilizing the brain-inspired and energy-efficient computational paradigm…
Yuanlin Yang, Chenhui Li, Xuhao Guo, Anqi Zhang +2
Biomedical regression tasks require predicting continuous targets from heterogeneous and unstructured evidence. While Large Language Models (LLMs) provide a robust interface for…
Ana Sanchez-Fernandez, Thomas Pinetz, Werner Zellinger, Günter Klambauer
Biomedical imaging data presents enormous potential for deep learning models to predict invaluable properties, such as diseases and drug effects. However, unavoidable alterations…
Xuanning Hu, Hao Tuo, Jinglong Ji, Anchen Li +2
Structure-based drug design (SBDD) can be effectively realized through an iterative refinement via the Design-Make-Test-Analyze (DMTA) cycle, which is a common workflow used by…
Kiet Bennema ten Brinke, Koen Minartz, Vlado Menkovski
Simulating trajectories of dynamical systems is a fundamental problem in a wide range of fields such as molecular dynamics, biochemistry, and pedestrian dynamics. Machine learning…
Daiheng Zhang, Shiyang Zhang, Sizhuang He, Yangtian Zhang +2
Discrete biological sequence optimization often requires goal-directed, parser-valid edits to an existing protein or molecule. Diffusion models support iterative refinement but do…
Tianqiu Zhang, Muyang Lyu, Xiao Liu, Si Wu
Humans abstract experiences into structured representations to facilitate pattern inference and knowledge transfer. While the hippocampal-entorhinal (HPC-MEC) circuit is known to…
Hongyu Shi, Kaizhong Zheng, Wensheng Zhai, Shuai Jiang +2
Multi-modal neuroimaging-based psychiatric diagnosis must integrate cross-modal agreement with modality-specific complementarity, yet in real multi-site cohorts these signals are…
Maxx Richard Rahman, Mostafa Hammouda, Wolfgang Maass
Large Language Models have shown strong generalization across natural language tasks but remain underexplored for longitudinal clinical profiles. In sports anti-doping, biological…
Bowen Jing, Mihir Bafna, Anisha Parsan, Heyuan Michael Ni +4
Multistate mechanisms underlie many of the complex functions observed in natural proteins. The ability to rationally design multistate proteins would have transformative…
Weiren zhao, DONG Yi, Cheng Chen
Unifying multimodal understanding and generation is a compelling frontier that is beginning to emerge in the medical field. However, the limited existing unified medical models…
Miruna Cretu, John Bradshaw, Patricia Suriana, Saeed Saremi +4
We present SynLaD, a latent diffusion framework for small-molecule generation that unifies ligand-based drug design objectives (what to make) with synthetic accessibility (how to…
Jin Gao, Juntu Zhao, Zirui Zeng, Jiaqi Shen +4
AI for scientific discovery is entering an agentic era, where protein-engineering systems are expected to prioritize future wet-lab experiments rather than merely fit static…
Mohammad Haddadnia, Yuvan Chali, Abhilash Jayaraj, Constance Kraay +3
Identifying high-utility candidates from massive discrete spaces under expensive evaluations is a recurring challenge across the sciences, with structure-based drug discovery as a…
Jiyeon Kim, Byungju Lee, Won-Yong Shin
Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate…
Matteo Rossi, Ryan Pederson, Miles Wang-Henderson, Ben Kaufman +4
We present TerraBind, a foundation model for protein-ligand structure and binding affinity prediction that achieves 26$\times$ faster inference than state-of-the-art methods while…
Erjian Zhang, Yatong Hao, Liejun Wang, Zhiqing Guo
While multi-task learning based automatic radiology report generation (RRG) is widely adopted to ensure clinical consistency, most focus on architectural designs yet remain…
Jiusong Ge, Yingkang Zhan, Wenjie Zhao, Di Zhang +4
Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level features at high magnification and…
Bong Gyun Kang, Junyong Ahn, Hyeongrok Han, Sungroh Yoon
Electronic Health Records (EHRs) possess unique characteristics distinct from natural language, yet existing EHR foundation models often rely on suboptimal NLP-based approaches.…
Kaitao Chen, Weiqian Zhao, Jiamin Wu, Qihao Zheng +4
Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medical images, which typically…
Jack Goffinet, Casey Hanks, David Carlson
Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication across brain regions.…
Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng +2
Proteins are shaped by gradual evolution under biophysical and functional constraints. Protein language models learn rich evolutionary constraints from large-scale sequences, and…
Haorui Wang, Parshin Shojaee, Kazem Meidani, Kunyang Sun +4
Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many…
Zekai Chen, Xunkai Li, Sirui Zhang, Henan Sun +4
De novo ligand design is a fundamental task that seeks to generate protein or molecule candidates that can effectively dock with protein receptors and achieve strong binding…
Jiaxin Qi, Hang Li, Yan Cui, Yuhua Zheng +1
Gene Regulatory Network (GRN) inference is essential for understanding complex cellular mechanisms, rendered tractable through single-cell transcriptomic data. With the emergence…
Moritz Schaefer, Zoe Piran, Nils Philipp Walter, Animesh Awasthi +3
The characterization of histopathology with AI promises to assist clinical decision-making, but it is currently limited due to coarse-grained annotations that miss cellular…
Shohaib Shaffiey, Massimiliano Pierobon
The fields of AI-based disease fingerprinting, drug discovery and repurposing are currently among the emerging frontiers of machine learning applied to medicine. One major…
Yaoyu Chen, Xiaoli Lin, Ziyi Gong, Jun Pang
Efficiently designing high-quality molecules targeting disease-relevant targets is a critical challenge. Most existing methods can capture pairwise amino acid relations,…
Nikita Makarov, Maria Bordukova, Lena Voith von Voithenberg, Estrella Pivel-Villanueva +4
Precision oncology requires forecasting clinical events and trajectories, yet modeling sparse, multi-modal clinical time series remains a critical challenge. We introduce…
David Aaron Koplow, Tomaso Poggio, Liu Ziyin
Hebbian and anti-Hebbian plasticity are widely observed in the brain and are classically modeled as mechanistic, local homosynaptic rules stabilized by homeostatic constraints.…
Tianhao Huang, Guanghui Min, Zhenyu Lei, Aiying Zhang +1
Unraveling how macroscopic cognitive phenotypes emerge from microscopic neuronal connectivity remains one of the core pursuits of neuroscience. To this end, researchers typically…
Junzhi Ning, Wei Li, Cheng Tang, Jiashi Lin +4
Medical workflows routinely combine reading images with producing visual and textual outputs, making both image understanding and generation central to medical AI. Most existing…
Chenyang Xu, Dezhen Wang, Hao Wang
Synthesizing authentic phonocardiograms (PCG) from ubiquitous electrocardiograms (ECG) is a critical task for accessible cardiac monitoring. Existing generative models, however,…
Yuchen Hua, Xingang Peng, Jianzhu Ma, Muhan Zhang
Generative modeling of three-dimensional (3D) molecules is a fundamental yet challenging problem in drug discovery and materials science. Existing approaches typically represent…
Minxu Liu, Donghai Guan, Chuhang Zheng, Chunwei Tian +2
Understanding and decoding brain activity into visual representations is a fundamental challenge at the intersection of neuroscience and artificial intelligence. While…
Xinyu Wang, Ruoyu Wang, Qiangwei Peng, Peijie Zhou +1
Reconstructing dynamical evolution from limited observations is a fundamental challenge in single-cell biology, where dynamic unbalanced optimal transport (OT) provides a…
Wenkang Jiang, Yuhang Liu, Yichao Cai, Erdun Gao +4
Single-cell perturbation modeling is fundamental for understanding and predicting cellular responses to genetic perturbations. However, existing approaches, from causal…
Bogdan Zagribelnyy, Ivan Ilin, Maksim Kuznetsov, Nikita Bondarev +4
Recent progress has expanded the use of large language models (LLMs) in drug discovery, including synthesis planning. However, objective evaluation of retrosynthesis performance…