Benchmarking network algorithms for contextualizing genes of interest.

Author summary In our labs, we aimed to use network algorithms to contextualize hits from functional genomics screens and gene expression studies. In order to understand how to apply these algorithms to our data, we characterized seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Computational approaches have shown promise in contextualizing genes of interest with known molecular interactions. In this work, we evaluate seventeen previously published algorithms based on characteristics of their output and their performance in three tasks: cross validation, prediction of drug targets, and behavior with random input. Our work highlights strengths and weaknesses of each algorithm and results in a recommendation of algorithms best suited for performing different tasks.