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Solving Generalized Maximum-Weight Connected Subgraph Problem for Network Enrichment Analysis

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Algorithms in Bioinformatics (WABI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9838))

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Abstract

Network enrichment analysis methods allow to identify active modules without being biased towards a priori defined pathways. One of mathematical formulations of such analysis is a reduction to a maximum-weight connected subgraph problem. In particular, in analysis of metabolic networks a generalized maximum-weight connected subgraph (GMWCS) problem, where both nodes and edges are scored, naturally arises. Here we present the first to our knowledge practical exact GMWCS solver. We have tested it on real-world instances and compared to similar solvers. First, the results show that on node-weighted instances GMWCS solver has a similar performance to the best solver for that problem. Second, GMWCS solver is faster compared to the closest analogue when run on GMWCS instances with edge weights.

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Funding

This work was supported by Government of Russian Federation [Grant 074-U01 to A.A.S., A.A.L.].

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Authors and Affiliations

  1. Computer Technologies Department, ITMO University, Saint Petersburg, 197101, Russia

    Alexander A. Loboda & Alexey A. Sergushichev

  2. Department of Pathology and Immunology, Washington University in St. Louis, St. Louis, MO, USA

    Maxim N. Artyomov

Authors
  1. Alexander A. Loboda
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  2. Maxim N. Artyomov
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  3. Alexey A. Sergushichev
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Corresponding author

Correspondence to Alexey A. Sergushichev .

Editor information

Editors and Affiliations

  1. AIST and University of Tokyo , Tokyo, Japan

    Martin Frith

  2. Aarhus University, Aarhus, Denmark

    Christian Nørgaard Storm Pedersen

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© 2016 Springer International Publishing Switzerland

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Loboda, A.A., Artyomov, M.N., Sergushichev, A.A. (2016). Solving Generalized Maximum-Weight Connected Subgraph Problem for Network Enrichment Analysis. In: Frith, M., Storm Pedersen, C. (eds) Algorithms in Bioinformatics. WABI 2016. Lecture Notes in Computer Science(), vol 9838. Springer, Cham. https://doi.org/10.1007/978-3-319-43681-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-43681-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43680-7

  • Online ISBN: 978-3-319-43681-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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