Integrating data and knowledge to identify functional modules of genes: a multilayer approach

Lifan Liang,Vicky Chen,Songjian Lu,Kunju Zhu,Xiaonan Fan,Xinghua Lu

doi:10.1186/s12859-019-2800-y

Abstract

BackgroundCharacterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Unfortunately, computational methods to identify functional modules were limited by the data quality issues of high-throughput techniques. This study aims to integrate knowledge extracted from literature to further improve the accuracy of functional module identification.ResultsOur new model and algorithm were applied to both yeast and human interactomes. Predicted functional modules have covered over 90% of the proteins in both organisms, while maintaining a comparable overall accuracy. We found that the combination of both mRNA expression information and biomedical knowledge greatly improved the performance of functional module identification, which is better than those only using protein interaction network weighted with transcriptomic data, literature knowledge, or simply unweighted protein interaction network. Our new algorithm also achieved better performance when comparing with some other well-known methods, especially in terms of the positive predictive value (PPV), which indicated the confidence of novel discovery.ConclusionHigher PPV with the multiplex approach suggested that information from both sources has been effectively integrated to reduce false positive. With protein coverage higher than 90%, our algorithm is able to generate more novel biological hypothesis with higher confidence.

Highlights

Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics
This study demonstrated that topic modeling of biomedical literature is an effective complementary source of information
In this paper, we have proposed a multiplex approach to integrate high-throughput data and literature for functional module identification and developed a clustering method that can utilize the topology based on random walk

Summary

Introduction

Characterizing the modular structure of cellular network is an important way to identify novel genes for targeted therapeutics. This is made possible by the rising of high-throughput technology. Cancer was often viewed as the disruption of cellular signaling networks Such complex networks are inherently modular [2], meaning that genes usually perform certain biological function in separate groups. A functional module is defined as a group of genes or their products which are related by one or more genetic or cellular interactions, e.g. co-regulation, co-expression or membership of a protein complex, of a metabolic or signaling pathway or of a cellular aggregate (e.g. chaperone, ribosome, protein transport facilitator) [3]. Et al [5] have found that functional interactions that are part of functional modules

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