Functional Interaction Network Construction and Analysis for Disease Discovery

Methods Mol Biol. 2017;1558:235-253. doi: 10.1007/978-1-4939-6783-4_11.

Abstract

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Keywords: Biological network; Biological pathway; Cytoscape; Functional interaction; Java; MySQL; Naïve Bayesian Classifier; Network-based analysis; Reactome; ReactomeFIViz.

MeSH terms

  • Bayes Theorem
  • Computational Biology / methods*
  • Data Mining / methods
  • Databases, Factual
  • Disease Susceptibility*
  • Gene Regulatory Networks
  • Humans
  • Metabolic Networks and Pathways
  • Molecular Sequence Annotation
  • Protein Interaction Maps
  • ROC Curve
  • Signal Transduction
  • Software*
  • Web Browser
  • Workflow