Abstract
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.
Publication types
-
Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
-
Algorithms*
-
Benchmarking
-
Computational Biology
-
Databases, Genetic / statistics & numerical data
-
Databases, Protein / statistics & numerical data
-
Gene Regulatory Networks*
-
Humans
-
Models, Genetic*
-
Protein Interaction Maps / genetics
Grants and funding
This research was funded by Novartis Institutes for BioMedical Research. Novartis provided support in the form of salaries for all authors. Army Research Office Institute for Collaborative Biotechnologies (W911NF-09-0001) funded the graduate school tuition of Abby Hill. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.