Deep functional synthesis: a machine learning approach to gene functional enrichment
Sheng Wang, Jianzhu Ma, Samson H. Fong, Stefano Rensi, Jiawei Han, Jian Xun Peng, Dexter R. Pratt, Russ Biagio Altman, Trey Ideker · bioRxiv (Cold Spring Harbor Laboratory) · 2019
ABSTRACT Gene functional enrichment is a mainstay of genomics, but it relies on manually curated databases of gene functions that are incomplete and unaware of the biological context. Here we present an alternative machine learning approach, Deep Functional Synthesis (DeepSyn), which moves beyond gene function databases to dynamically infer the functions of a gene set from its associated network of literature and data, conditioned on the disease and drug context of the current experiment. Using a knowledge graph with 3,048,803 associations between genes, diseases, drugs, and functions, DeepSyn obtained accurate performance (range 0.74 AUC to 0.96 AUC) on a variety of biological applications including drug target identification, gene set functional enrichment, and disease gene prediction. Availability The DeepSyn codebase is available on GitHub at http://github.com/wangshenguiuc/DeepSyn/ under an open source distribution license.