AI and the World's Hard Problems · Life sciences · Nature (machine learning) · 2026-10-16
LitGene: an interpretable transformer model integrating text and ontology for gene representation learning
Gene representation learning often overlooks the scientific literature, which can bias results against understudied genes. LitGene is an interpretable transformer that integrates text with Gene Ontology annotations through contrastive learning. It achieved competitive accuracy on eight protein benchmarks, zero-shot predicted GO terms, KEGG pathways and gene-disease links, and flagged ANGPTL6 as a candidate gene for idiopathic pulmonary fibrosis, supported by differential expression in independent samples.