RoohaniYH, HuaTJ, TungP-Y, et al.Virtual cell challenge: Toward a turing test for the virtual cell. Cell, 2025; 188(13):3370–3374; doi: 10.1016/j.cell.2025.06.008
2.
IstrateA-M, LiD, KaraletsosT. scGenePT: Is language all you need for modeling single-cell perturbations? bioRxiv, 2024; doi: 10.1101/2024.10.23.619972
3.
PearceJD, SimmondsSE, MahmoudabadiG, et al.A cross-species generative cell atlas across 1.5 billion years of evolution: The transcriptformer single-cell model. bioRxiv, 2025; doi: 10.1101/2025.04.25.650731
4.
GrinsteinJD. Ahead of the curve: The arc institute aims to reshape the biomedical research landscape. GEN Biotech, 2022; 1(2):115–119; doi: 10.1089/genbio.2022.29017.jgr
5.
BrixiG, DurrantMG, KuJ, et al.Genome modeling and design across all domains of life with Evo 2. bioRxiv, 2025; doi: 10.1101/2025.02.18.638918
6.
AdduriAK, GautamD, BevilacquaB, et al.Predicting cellular responses to perturbation across diverse contexts with STATE. bioRxiv, 2025; doi: 10.1101/2025.06.26.661135
7.
YoungblutND, CarpenterC, PrasharJ, et al.scBaseCount: An AI agent-curated, uniformly processed, and continually expanding single cell data repository. bioRxiv, 2025; doi: 10.1101/2025.02.27.640494
8.
ZhangJ, UbasAA, de BorjaR, et al.Tahoe-100M: A giga-scale single-cell perturbation atlas for context-dependent gene function and cellular modeling. bioRxiv, 2025; doi: 10.1101/2025.02.20.639398
9.
KleinD, FleckJS, BobrovskiyD, et al.CellFlow enables generative single-cell phenotype modeling with flow matching. bioRxiv, 2025; doi: 10.1101/2025.04.11.648220
10.
HuangAC, HsiehT-HS, ZhuJ, et al.X-Atlas/Orion: Genome-wide Perturb-seq datasets via a scalable fix-cryopreserve platform for training dose-dependent biological foundation models. bioRxiv, 2025; doi: 10.1101/2025.06.11.659105
11.
CuiH, WangC, MaanH, et al.scGPT: Toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods, 2024; 21(8):1470–1480; doi: 10.1038/s41592-024-02201-0
