Aviv Regev

The paper introduces CellMAPP and CHART to analyze multiplexed antibody imaging data, revealing modular morphological programs in response to genetic perturbations.

The paper integrates multiple single-cell and single-nucleus RNA-sequencing datasets to create a comprehensive Human Kidney Cell Atlas, identifying cell types, states, and disease-associated populations.

AutoScreen improves gene prioritization in functional genomics by integrating AI agents for pre-screen design and post-screen analysis, achieving higher hit recovery and precision than existing methods.

Perturb-ME combines CRISPR screening and single-cell profiling to identify regulatory modules governing gene programs in melanoma.

Orion, an AI agent, automates biomedical image analysis by integrating large language models with software execution and reasoning, generating research reports and hypotheses from imaging data.

SCALLOPS improves processing of optical pooled screens by enabling efficient alignment and analysis of large-scale image data, demonstrating superior performance over existing methods.

The paper introduces PerturbPair, a platform integrating transcriptomic and optical screens to map macrophage regulatory networks, revealing cross-modal relationships and disease-relevant gene programs.

The paper introduces a framework for multimodal chemical screens combining RNA, protein, and epigenomic readouts, revealing compound-specific mechanisms and off-target effects in T cells.

This study uses single-cell and spatial profiling to show that inflammatory signals from macrophages and fibroblasts drive CD8 T cell exhaustion and resistance to immune checkpoint blockade in prostate cancer.

This paper develops computational tools for allelic imbalance analysis using single-nucleus RNA-seq and demonstrates their application in a Parkinson's disease cohort.

PaSCient learns patient-level disease representations from scRNA-seq data, enabling disease classification and downstream analyses like dimensionality reduction and patient subgroup discovery.

This paper introduces UTMM, a method to simultaneously measure transcriptomes and mechanical properties in intact embryos, revealing coupling between RNA expression and intracellular compliance during early development.

DISCO-seq enables 3D single-cell transcriptomics of whole tissues and organisms by combining tissue-clearing chemistry with imaging and transcriptomic profiling.

CellUntangler disentangles multiple biological signals in single-cell data using deep generative models, separating cell cycle from other signals like cell type and differentiation.

This study creates a spatial and cellular atlas of the colon across age groups using spatial transcriptomics and single nucleus RNA-seq, identifying age-related molecular gradients and multicellular programs.

This study shows human brain organoids can mature over 5 years in culture, transcriptionally aging and retaining memory of time spent in vitro.

This paper introduces RECODR, a computational pipeline that tracks transcriptome changes in cancer development and treatment to predict resistance mechanisms and design effective therapies.

The paper shows that current cell embedding evaluation metrics can be misleading and introduces a new metric, scGraph, to detect distortions in embeddings.

PRESAGE predicts perturbation expression responses using gene embeddings and outperforms existing methods in functional evaluations.

An LLM agent with a literature-mined environment of 150 tools, 105 packages and 59 databases plans in code, matching reported human accuracy on database and sequence question answering.

PerTurboAgent predicts gene panels for sequential Perturb-Seq experiments using an LLM-based agent, outperforming existing strategies in identifying phenotypically impactful genes.

SlideCNA detects spatial copy number alterations from Slide-seq data to identify sub-clones in metastatic breast cancer.

The paper identifies genetic variants affecting innate lymphoid cells in the gut using single-cell RNA-seq and validates their regulatory roles across cell types.

CellFlow predicts single-cell phenotypes from complex perturbations using flow matching, demonstrating accuracy in cytokine stimulation, drug treatments, and developmental modeling.

SpatialAgent integrates large language models with dynamic tool retrieval, multimodal interpretation, and claim verification to automate end-to-end spatial biology workflows from gene-panel design to tissue annotation and hypothesis generation.

This study creates a single-nucleus and spatial transcriptomic atlas of the COVID-19 liver, revealing cellular and functional disruptions associated with organ damage.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

PathOmCLIP infers spatial gene expression from histology images by combining pathology and single-cell RNA-seq foundation models, achieving better gene expression prediction than existing methods.

The paper evaluates and optimizes sequence-based gene regulatory deep learning models using a DREAM Challenge dataset, showing improvements through architectural and training strategy refinements.

PerturbView enables highly multiplexed optical pooled screens in primary cells and tissues by improving barcode amplification, revealing regulators of NFκB signaling and enabling spatial transcriptomics integration.