Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Justin K Huang; Daniel E Carlin; Michael Ku Yu; Wei Zhang; Jason F Kreisberg; Pablo Tamayo; Trey Ideker

doi:10.1016/j.cels.2018.03.001

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes

Cell Syst. 2018 Apr 25;6(4):484-495.e5. doi: 10.1016/j.cels.2018.03.001. Epub 2018 Mar 28.

Authors

Justin K Huang¹, Daniel E Carlin², Michael Ku Yu³, Wei Zhang², Jason F Kreisberg², Pablo Tamayo⁴, Trey Ideker⁵

Affiliations

¹ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: jkh013@ucsd.edu.
² School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
³ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; School of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
⁴ School of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA.
⁵ Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA 92093, USA; School of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA.

Abstract

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research.

Keywords: genome-wide disease association; molecular networks; network analysis; network propagation.

MeSH terms

Algorithms
Computational Biology
Gene Regulatory Networks*
Genetic Predisposition to Disease*
Genome, Human
Humans

Abstract

MeSH terms

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