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Identifying dense subgraphs in protein–protein interaction network for gene selection from microarray data

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Abstract

Selection of important genes responsible for a disease is an important task in bioinformatics. Microarray data are often used with differential expression being considered as a cue. Recently, such expression data are supplemented by gene ontology and genes/proteins interaction network for the selection task. The functional knowledge and interaction structure have become critical for understanding the biological processes, including selection of genes potentially associated to complex diseases. In this paper, we propose an approach that combines expression analysis with structural analysis of protein–protein interaction networks to identify genes associated with complex diseases. The dense subgraph structures embedded in the networks are extracted. We present results on three different types of benchmark cancer dataset (prostate cancer, interstitial lung disease and chronic lymphocytic leukemia) and show that several interesting biological information could be inferred, besides achieving a high prediction accuracy. The proposed methodology helps to identify not just differentially expressed genes but also hub genes important in biological processes.

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Notes

  1. http://www.ncbi.nlm.nih.gov/geo/.

  2. http://www.ncbi.nlm.nih.gov/geo/.

  3. http://www.hprd.org/.

  4. http://www.ebi.ac.uk/intact/.

  5. http://www.mint.bio.uniroma2.it/mint/.

  6. http://www.ncbi.nlm.nih.gov/gene/.

  7. http://llama.med.harvard.edu/funcassociate.

  8. http://www.kegg.jp.

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Acknowledgments

We would like to thank the financial support from CAPES, CNPq, FAPESP (Grant 2011/50761-2), FAPESP-Microsoft (Grant 2010/52138-8), eScience-PRP-USP and Indo-Brazil Collaborative Project, DST, Govt. of India and Govt. of Brazil.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, 721302, Kharagpur, India

    Tripti Swarnkar & Pabitra Mitra

  2. Institute of Technical Education and Research, SOA University, 751030, Bhubaneswar, India

    Tripti Swarnkar

  3. Institute of Mathematics and Statistics, University of São Paulo, São Paulo, SP, Brazil

    Sergio Nery Simões & Ronaldo Fumio Hashimoto

  4. Federal Institute of Espírito Santo, Serra, ES, Brazil

    Sergio Nery Simões

  5. School of Medical Science and Technology, Indian Institute of Technology, 721302, Kharagpur, India

    Anji Anura & Jyotirmoy Chatterjee

  6. Institute of Psychiatry, University of São Paulo, São Paulo, SP, Brazil

    Helena Brentani

  7. Center of Mathematics, Computing and Cognition, Federal University of ABC, Santo André, SP, Brazil

    David Correa Martins

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  1. Tripti Swarnkar
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Correspondence to David Correa Martins.

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Swarnkar, T., Simões, S.N., Anura, A. et al. Identifying dense subgraphs in protein–protein interaction network for gene selection from microarray data. Netw Model Anal Health Inform Bioinforma 4, 33 (2015). https://doi.org/10.1007/s13721-015-0104-3

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