Research in the Gillis lab centers on using computational methods to understand gene function. Computational biology has taken up the challenge of determining gene function mainly by attempting to interpret the activities of genes in the context of networks derived from gene association data. As data sets characterizing genes grow in size and complexity, it seems self-evident that computation can assist in inference as to gene function. Gene network analysis intended to provide insight into complex disorders is a dominant interest in the field. Gene associations (of various sorts) are believed to encode functional interaction, and this interaction is frequently shown to be able to substantially predict gene function across all functions. This approach, commonly called “Guilt by Association” (GBA), is embedded in everything from prioritization of de novo variants to uncovering novel molecular phenotypes or mechanisms of disease. Our research focuses on identifying limitations in the GBA approach and making fundamental improvements to its operation for the interpretation of neuropsychiatric genomics data. Broadly, our research can be divided into gene network development and analysis, meta-analysis of gene function prediction algorithms (machine learning), and targeted methods applications.