Year: 2025
Type: preprint
Abstract: Functional gene annotation is a highly manual and subjective process that requires analysis of large amounts of statistical and literature results. Here we present Artificial Intelligence Gene Enrichment (AIGE), a careful automation of the current state of the art process used by bioinformatic experts to deter-mine functions enriched in novel or experimentally derived gene lists. In 1206 test cases, AIGE is able to accurately recover 87% of biological functions, path-ways, and cell types, is robust to noise contamination, and accurately assesses its own self-confidence. AIGE reports provide accurate functional annotations and encourage further research in a broad set of contexts.
Authors: Calvin Boyce, Clara Pereira, Gyeong Dae Kim, Allegra Petti
Institution: Mass General Brigham
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4413109817