ArticleNature communications2024
Genomic language model predicts protein co-regulation and function.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 2 of them syntheses that pooled it.
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Who cites it
45 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- (Re)defining the human chromatome: an integrated meta-analysis of localization, function, abundance, physical properties, and domain composition of chromatin proteins.Nucleic acids research · 2026Pooled it
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.Biochemical genetics · 2026Article
- Foundation Models for Microbiome Research: From Sequence Semantics to Community Dynamics and Multimodal World Models.Advanced genetics (Hoboken, N.J.) · 2026Review
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- AI-empowered human microbiome research.Gut · 2026Review
- PlasmidGPT: A generative framework for plasmid analysis and generation.Science advances · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- MGM as a Large-Scale Pretrained Foundation Model for Microbiome Analyses in Diverse Contexts.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Bacterial proteome foundation model enhances functional prediction from enzymes to ecological interactions.bioRxiv : the preprint server for biology · 2026Article
- Discovery of a phenazine-thiol conjugase from sparse data using genome-informed machine learning.bioRxiv : the preprint server for biology · 2026Article
- A Framework for Autonomous AI-Driven Drug Discovery.bioRxiv : the preprint server for biology · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- EXOSC3 G191 Variants Trigger System-Wide Recalibration of RNA Processing Machinery.bioRxiv : the preprint server for biology · 2026Article
- Extending protein language models to a viral genomic scale using biologically induced sparse attention.GigaScience · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- A comprehensive survey of genome language models in bioinformatics.Briefings in bioinformatics · 2026Review
- Know your scientist: KYC as biosecurity infrastructure.Frontiers in microbiology · 2026Article
- Viromics approaches for the study of viral diversity and ecology in microbiomes.Nature reviews. Genetics · 2026Review
- Large-scale multi-omic biosequence transformers for modeling protein-nucleic acid interactions.PloS one · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Deciphering the relationship between a gene and its genomic context is fundamental to understanding and engineering biological systems. Machine learning has shown promise in learning latent relationships underlying the sequence-structure-function paradigm from massive protein sequence datasets. However, to date, limited attempts have been made in extending this continuum to include higher order genomic context information. Evolutionary processes dictate the specificity of genomic contexts in which a gene is found across phylogenetic distances, and these emergent genomic patterns can be leveraged to uncover functional relationships between gene products. Here, we train a genomic language model (gLM) on millions of metagenomic scaffolds to learn the latent functional and regulatory relationships between genes. gLM learns contextualized protein embeddings that capture the genomic context as well as the protein sequence itself, and encode biologically meaningful and functionally relevant information (e.g. enzymatic function, taxonomy). Our analysis of the attention patterns demonstrates that gLM is learning co-regulated functional modules (i.e. operons). Our findings illustrate that gLM's unsupervised deep learning of the metagenomic corpus is an effective and promising approach to encode functional semantics and regulatory syntax of genes in their genomic contexts and uncover complex relationships between genes in a genomic region.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.