Inferring subnetworks from perturbed expression profiles. - Dana Pe'er; Aviv Regev; Gal Elidan; Nir Friedman (ISMB (Supplement of Bioinformatics), 2001)

Representation and Simulation of Biochemical Processes Using the pi-Calculus Process Algebra. - Aviv Regev; William Silverman; Ehud Shapiro (Pacific Symposium on Biocomputing, 2001)

Application of a stochastic name-passing calculus to representation and simulation of molecular processes. - Corrado Priami; Aviv Regev; Ehud Shapiro; William Silverman (Inf. Process. Lett., 2001)

Minreg: Inferring an active regulator set. - Dana Pe'er; Aviv Regev; Amos Tanay (ISMB, 2002)

Inferring quantitative models of regulatory networks from expression data. - Iftach Nachman; Aviv Regev; Nir Friedman (ISMB/ECCB (Supplement of Bioinformatics), 2004)

BioAmbients: an abstraction for biological compartments. - Aviv Regev; Ekaterina M. Panina; William Silverman; Luca Cardelli; Ehud Shapiro (Theor. Comput. Sci., 2004)

Learning Module Networks. - Eran Segal; Dana Pe'er; Aviv Regev; Daphne Koller; Nir Friedman (J. Mach. Learn. Res., 2005)

MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals. - Dana Pe'er; Amos Tanay; Aviv Regev (J. Mach. Learn. Res., 2006)

Automatic genome-wide reconstruction of phylogenetic gene trees. - Ilan Wapinski; Avi Pfeffer; Nir Friedman; Aviv Regev (ISMB/ECCB (Supplement of Bioinformatics), 2007)

BRNI: Modular analysis of transcriptional regulatory programs. - Iftach Nachman; Aviv Regev (BMC Bioinform., 2009)

Metabolic Network Analysis Demystified. - Leonid Chindelevitch; Aviv Regev; Bonnie Berger (RECOMB, 2011)

Physical Module Networks: an integrative approach for reconstructing transcription regulation. - Noa Novershtern; Aviv Regev; Nir Friedman (Bioinform., 2011)

A single-cell survey of the small intestinal epithelium. - Adam L. Haber; Moshe Biton; Noga Rogel; Rebecca H. Herbst; Karthik Shekhar; Christopher Smillie; Grace Burgin; Toni M. Delorey; Michael R. Howitt; Yarden Katz; Itay Tirosh; Semir Beyaz; et al. (Nat., 2017)

BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization. - Carl G. de Boer; Aviv Regev (BMC Bioinform., 2018)

Reconstructing probabilistic trees of cellular differentiation from single-cell RNA-seq data. - Miriam Shiffman; William T. Stephenson; Geoffrey Schiebinger; Jonathan H. Huggins; Trevor Campbell; Aviv Regev; Tamara Broderick (CoRR, 2018)

A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. - Daniel T. Montoro; Adam L. Haber; Moshe Biton; Vladimir Vinarsky; Brian Lin; Susan E. Birket; Feng Yuan; Sijia Chen; Hui Min Leung; Jorge Villoria; Noga Rogel; Grace Burgin; et al. (Nat., 2018)

Resolving medulloblastoma cellular architecture by single-cell genomics. - Volker Hovestadt; Kyle S. Smith; Laure Bihannic; Mariella G. Filbin; McKenzie L. Shaw; Alicia Baumgartner; John C. DeWitt; Andrew Groves; Lisa Mayr; Hannah R. Weisman; Alyssa R. Richman; Marni E. Shore; et al. (Nat., 2019)

Decoding human fetal liver haematopoiesis. - Dorin-Mirel Popescu; Rachel A. Botting; Emily Stephenson; Kile Green; Simone Webb; Laura Jardine; Emily F. Calderbank; Krzysztof Polanski; Issac Goh; Mirjana Efremova; Meghan Acres; Daniel Maunder; et al. (Nat., 2019)

The human body at cellular resolution: the NIH Human Biomolecular Atlas Program. - Michael P. Snyder; Shin Lin; Amanda Posgai; Mark Atkinson; Aviv Regev; Jennifer Rood; Orit Rozenblatt-Rosen; Leslie Gaffney; Anna Hupalowska; Rahul Satija; Nils Gehlenborg; Jay Shendure; et al. (Nat., 2019)

Inherent Limitations of Parameter Estimation of a Tempo-Spatial Field Using an Array of Heterogeneous Sensors. - Aviv Regev; Jonatan Ostrometzky; Hagit Messer (EUSIPCO, 2021)

Inference of cell dynamics on perturbation data using adjoint sensitivity. - Weiqi Ji; Bo Yuan; Ciyue Shen; Aviv Regev; Chris Sander; Sili Deng (CoRR, 2021)

Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI. - Ethan Weinberger; Romain Lopez; Jan-Christian Hütter; Aviv Regev (MLCB, 2022)

Large-Scale Differentiable Causal Discovery of Factor Graphs. - Romain Lopez; Jan-Christian Hütter; Jonathan K. Pritchard; Aviv Regev (NeurIPS, 2022)

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. - Romain Lopez; Natasa Tagasovska; Stephen Ra; Kyunghyun Cho; Jonathan K. Pritchard; Aviv Regev (CLeaR, 2023)

A Supervised Contrastive Framework for Learning Disentangled Representations of Cellular Perturbation Data. - Xinming Tu; Jan-Christian Hütter; Zitong Jerry Wang; Takamasa Kudo; Aviv Regev; Romain Lopez (MLCB, 2023)

Toward the Identifiability of Comparative Deep Generative Models. - Romain Lopez; Jan-Christian Hütter; Ehsan Hajiramezanali; Jonathan K. Pritchard; Aviv Regev (CLeaR, 2024)

Sequential Optimal Experimental Design of Perturbation Screens Guided by Multi-modal Priors. - Kexin Huang; Romain Lopez; Jan-Christian Hütter; Takamasa Kudo; Antonio Rios; Aviv Regev (RECOMB, 2024)

Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding. - Xiner Li; Yulai Zhao 002; Chenyu Wang 003; Gabriele Scalia; Gökcen Eraslan; Surag Nair; Tommaso Biancalani; Aviv Regev; Sergey Levine; Masatoshi Uehara (CoRR, 2024)

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities. - Charlotte Bunne; Yusuf H. Roohani; Yanay Rosen; Ankit Gupta; Xikun Zhang 001; Marcel Roed; Theo Alexandrov; Mohammed AlQuraishi; Patricia Brennan; Daniel B. Burkhardt; Andrea Califano; Jonah Cool; et al. (CoRR, 2024)

Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport. - Jayoung Ryu; Charlotte Bunne; Luca Pinello; Aviv Regev; Romain Lopez (AISTATS, 2025)

Modeling Complex System Dynamics with Flow Matching Across Time and Conditions. - Martin Rohbeck; Edward De Brouwer; Charlotte Bunne; Jan-Christian Huetter; Anne Biton; Kelvin Y. Chen; Aviv Regev; Romain Lopez (ICLR, 2025)

Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design. - Chenyu Wang 003; Masatoshi Uehara; Yichun He; Amy Wang; Avantika Lal; Tommi S. Jaakkola; Sergey Levine; Aviv Regev; Hanchen Wang 002; Tommaso Biancalani (ICLR, 2025)

Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States. - Tianyu Liu 001; Edward De Brouwer; Tony Kuo; Nathaniel Diamant; Alsu Missarova; Hanchen Wang 002; Minsheng Hao; Héctor Corrada Bravo; Gabriele Scalia; Aviv Regev; Graham Heimberg (RECOMB, 2025)

Inference-Time Alignment in Diffusion Models with Reward-Guided Generation: Tutorial and Review. - Masatoshi Uehara; Yulai Zhao 002; Chenyu Wang 003; Xiner Li; Aviv Regev; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Supervised Contrastive Block Disentanglement. - Taro Makino; Ji Won Park; Natasa Tagasovska; Takamasa Kudo; Paula Coelho; Jan-Christian Huetter; Heming Yao; Burkhard Hoeckendorf; Ana Carolina Leote; Stephen Ra; David Richmond; Kyunghyun Cho; et al. (CoRR, 2025)

Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design. - Masatoshi Uehara; Xingyu Su; Yulai Zhao 002; Xiner Li; Aviv Regev; Shuiwang Ji; Sergey Levine; Tommaso Biancalani (CoRR, 2025)

Dynamic Search for Inference-Time Alignment in Diffusion Models. - Xiner Li; Masatoshi Uehara; Xingyu Su; Gabriele Scalia; Tommaso Biancalani; Aviv Regev; Sergey Levine; Shuiwang Ji (CoRR, 2025)

Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning. - Hugues Van Assel; Mark Ibrahim; Tommaso Biancalani; Aviv Regev; Randall Balestriero (CoRR, 2025)