Machine learning for single-cell data and cell atlases.
26 papers in this topic (ICML 2026).
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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,…
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…
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…
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…
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…
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…
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…
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…
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…
Metadata from BioTender-max/icml2026-ai-bio (CC0-1.0).