新しいAI技術で細胞内の働きを予測 (New AI Predicts Inner Workings of Cells)

2025-01-08 コロンビア大学

<関連情報>

• https://www.cuimc.columbia.edu/news/new-ai-predicts-inner-workings-cells#
• https://www.nature.com/articles/s41586-024-08391-z

ヒト細胞型における転写の基礎モデル A foundation model of transcription across human cell types

Xi Fu,Shentong Mo,Alejandro Buendia,Anouchka P. Laurent,Anqi Shao,Maria del Mar Alvarez-Torres,Tianji Yu,Jimin Tan,Jiayu Su,Romella Sagatelian,Adolfo A. Ferrando,Alberto Ciccia,Yanyan Lan,David M. Owens,Teresa Palomero,Eric P. Xing & Raul Rabadan
Nature  Published:08 January 2025
DOI:https://doi.org/10.1038/s41586-024-08391-z

Abstract

Transcriptional regulation, which involves a complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate to unseen cell types and conditions. Here we introduce GET (general expression transformer), an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types^1,2. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types³. GET also shows remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovers universal and cell-type-specific transcription factor interaction networks. We evaluated its performance in prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors and found that it outperforms current models⁴ in predicting lentivirus-based massively parallel reporter assay readout^5,6. In fetal erythroblasts⁷, we identified distal (greater than 1 Mbp) regulatory regions that were missed by previous models, and, in B cells, we identified a lymphocyte-specific transcription factor–transcription factor interaction that explains the functional significance of a leukaemia risk predisposing germline mutation^8,9,10. In sum, we provide a generalizable and accurate model for transcription together with catalogues of gene regulation and transcription factor interactions, all with cell type specificity.