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Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming

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  • Hossein Shahrabi Farahani
  • Jens Lagergren

Abstract

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Suggested Citation

  • Hossein Shahrabi Farahani & Jens Lagergren, 2013. "Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    • Handle: RePEc:plo:pone00:0065773
    DOI: 10.1371/journal.pone.0065773
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    Cited by:

    1. Salim Akhter Chowdhury & Stanley E Shackney & Kerstin Heselmeyer-Haddad & Thomas Ried & Alejandro A Schäffer & Russell Schwartz, 2014. "Algorithms to Model Single Gene, Single Chromosome, and Whole Genome Copy Number Changes Jointly in Tumor Phylogenetics," PLOS Computational Biology, Public Library of Science, vol. 10(7), pages 1-19, July.
    2. Sahand Khakabimamaghani & Dujian Ding & Oliver Snow & Martin Ester, 2019. "Uncovering the subtype-specific temporal order of cancer pathway dysregulation," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-19, November.
    3. Mohammadreza Mohaghegh Neyshabouri & Seong-Hwan Jun & Jens Lagergren, 2020. "Inferring tumor progression in large datasets," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-16, October.

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