5
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications

      ,
      Journal of Molecular Biology
      Elsevier BV

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <p class="first" id="P2">Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunctional mechanistic pathways. Identifying such disease modules will provide insights into a systems-level understanding of molecular mechanisms of diseases. Owing to the incompleteness of our knowledge of disease proteins and limited information on the biological mediators of pathobiological processes, the key proteins (seed proteins) for many diseases appear scattered over the human protein-protein interactome and form a few small branches, rather than coherent network modules. In this paper, we develop a network-based algorithm, called Seed Connector algorithm (SCA), to pinpoint disease modules by adding as few additional linking proteins (seed connectors) to the seed protein pool as possible. Such seed connectors are hidden disease module elements that are critical for interpreting the functional context of disease proteins. The SCA aims to connect seed disease proteins so that disease mechanisms and pathways can be decoded based on predicted coherent network modules. We validate the algorithm using a large corpus of 70 complex diseases and binding targets of over 200 drugs, and demonstrate the biological relevance of the seed connectors. Lastly, as a specific proof-of-concept, we apply SCA to a set of seed proteins for coronary artery disease (CAD) derived from a meta-analysis of large-scale genome-wide association studies (GWAS) and obtain a CAD module enriched with important disease-related signaling pathways and drug targets not previously recognized. </p><p id="P3"> <div class="figure-container so-text-align-c"> <img alt="" class="figure" src="/document_file/728f95f6-e0bd-4e55-bc69-592ed532a325/PubMedCentral/image/nihms969468u1.jpg"/> </div> </p>

          Related collections

          Author and article information

          Journal
          Journal of Molecular Biology
          Journal of Molecular Biology
          Elsevier BV
          00222836
          September 2018
          September 2018
          : 430
          : 18
          : 2939-2950
          Article
          10.1016/j.jmb.2018.05.016
          6097931
          29791871
          dadc3688-3fb6-40b0-9019-a3c660485fe9
          © 2018

          https://www.elsevier.com/tdm/userlicense/1.0/

          History

          Comments

          Comment on this article