New artificial intelligence model maps how genes work together inside cells
New artificial intelligence model maps how genes work together inside cells
Publish Date: 2026-05-21 14:26:00
Source Domain: www.news-medical.net
Scientists at the Icahn School of Medicine at Mount Sinai have created a new artificial intelligence (AI) model that helps reveal how genes function together inside human cells, offering a powerful new way to understand biology and disease.
The study, published in the May 21 online issue of Patterns, a Cell Press Journal [https://doi.org/10.1016/j.patter.2026.101565], introduces a gene set foundation model (GSFM) designed to learn patterns in how genes are grouped and function across thousands of biological contexts. The work draws inspiration from advances in large language models (LLMs) such as ChatGPT, which learn how words gain meaning depending on their context. In a similar way, a GSFM learns how genes behave differently depending on their cellular “context.”
Genes rarely act alone. Instead, they participate in multiple biological processes, forming different molecular groupings depending on where and when they are active in the cell. A single gene can play different roles in different settings, much like a word can have different meanings in different sentences. Just as modern language models learn the meaning of words from context, we asked whether AI could learn the ‘meaning’ of genes in the same way. Our GSFM was designed to do exactly that.”
Avi Ma’ayan, PhD, senior corresponding author, Professor of Pharmacological Sciences and Director of the Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai
The model provides a new way to understand the structural and functional organization of genes and their products inside human cells. This improved understanding could eventually support the development of better diagnostics, biomarkers, and therapies. By mapping how genes relate to one another across many biological situations, the GSFM creates a reference framework that can help scientists interpret complex multi-omics datasets more effectively, say the investigators.
“The organization of genes within…
