arXiv Machine Learning

Uncovering smooth structures in single-cell data with PCS-guided neighbor embeddings

arXiv:2506. 22228v2 Announce Type: replace-cross Abstract: Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions.

arXiv Machine Learning

Jun 9

scCBGM: Interpretable Single-Cell Counterfactual Editing

arXiv:2606. 07760v1 Announce Type: new Abstract: Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design.

By Alma Andersson, Aya Abdelsalam Ismail, Edward De Brouwer, Doron Haviv, Tommaso Biancalani, Kyunghyun Cho, Gabriele Scalia, A\"icha BenTaieb, Hector Corrada Bravo

diffusion benchmarks

arXiv Machine Learning

Jun 29

PerturbCellRL: Verifier-Guided Reinforcement Learning for Single-Cell Perturbation Prediction

arXiv:2606. 27752v1 Announce Type: new Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions.

By Dongxia Wu, Mingyu Li, Yuhui Zhang, Anurendra Kumar, Emma Lundberg, Serena Yeung-Levy, Emily B. Fox

reinforcement-learning benchmarks safety

arXiv AI

Jun 11

FreeBridge: Variational Schr\"odinger Bridges for Cellular Transition Dynamics

arXiv:2606. 11286v1 Announce Type: cross Abstract: High-content imaging assays quantify cellular responses to chemical and genetic perturbations, yet continuous trajectories of individual cells are unobservable because cells are chemically fixed at acquisition.

By Xurui Wang, Qin Ren, Jun Ma, Haibin Ling, Chenyu You

safety

arXiv AI

Jun 12

OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction

arXiv:2606. 12838v1 Announce Type: cross Abstract: Predicting single-cell transcriptional responses to genetic, chemical and cytokine perturbations is a fundamental challenge in computational biology and AI Virtual Cell (AIVC) modeling, with direct implications for drug discovery and the elucidation of gene regulatory networks.

By Danning Jiang, Zheming An, Yalong Zhao, Lipeng Lai

llms rag benchmarks

arXiv AI

Jun 9

Single-Cell Cross-Modal Transfer by Adversarial Fine-Tuning of Foundation Models

arXiv:2606. 07676v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) is a powerful tool for exploring biological properties dependent on structure, proximity, and interaction in tissue.

By Joseph Boyd, Matthew Lyon, Martino Mansoldo, Christian Hurry, Finnian Firth

fine-tuning multimodal safety

arXiv Machine Learning

6d ago

CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data

arXiv:2608. 11269v1 Announce Type: cross Abstract: Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-dimensional representation challenging.

By Fenosoa Randrianjatovo, Maya Saleh, Simon Girard, Amadou Barry

rag benchmarks

arXiv Machine Learning

1d ago

A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data

arXiv:2608. 15306v1 Announce Type: cross Abstract: High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time.

By Mary Chriselda Antony Oliver, Kaitlyn Hohmeier, Tuyen Tran, Alejandra Castillo, Caroline Moosm\"uller, Shiying Li

rag efficiency

arXiv AI

Jun 12

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

arXiv:2606. 13007v1 Announce Type: cross Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity.

By Ping Xu, Pengjiang Li, Tian Du, Zaitian Wang, Jiawei Gu, Ziyue Qiao, Pengfei Wang, Yuanchun Zhou

llms rag diffusion multimodal benchmarks safety

arXiv Machine Learning

Aug 4

Beyond Gene Reconstruction: Learning Cell Representations through Complementary Transcriptomic Views

arXiv:2608. 00985v1 Announce Type: new Abstract: The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expression values.

By Jiaqi Xiong, Yuntao hu, Yu Zheng, Yifei Shi, Xinyue Guo, Jiaxin Qi

arXiv Machine Learning

Jun 5

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data

arXiv:2506. 11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level.

By Hiren Madhu, Jo\~ao Felipe Rocha, Tinglin Huang, Siddharth Viswanath, Smita Krishnaswamy, Rex Ying

llms rag benchmarks

arXiv Machine Learning

Jul 9

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data

arXiv:2607. 06583v1 Announce Type: cross Abstract: DNA methylation (DNAm) serves as one of the most robust molecular biomarkers of biological aging.

By Chandan Gupta, Syed Haider, Pietro Li\`o

diffusion safety

arXiv Machine Learning

Jun 17

Beyond Independent Genes: Learning Module-Inductive Representations for Single-Cell Gene Perturbation Prediction

arXiv:2602. 04901v2 Announce Type: replace-cross Abstract: Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics.

By Jiafa Ruan, Ruijie Quan, Liyang Xu, Zongxin Yang, Yi Yang

diffusion benchmarks safety

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