Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
2013 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 6, e65773- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2013. Vol. 8, no 6, e65773- p.
Keyword [en]
Cancer, Bayesian networks
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-121701DOI: 10.1371/journal.pone.0065773ISI: 000320363300042Scopus ID: 2-s2.0-84879060692OAI: oai:DiVA.org:kth-121701DiVA: diva2:619425
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20131121. Updated from manuscript to article in journal.

Available from: 2013-05-03 Created: 2013-05-03 Last updated: 2017-12-06Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopus

Search in DiVA

By author/editor
Shahrabi Farahani, HosseinLagergren, Jens
By organisation
Computational Biology, CBScience for Life Laboratory, SciLifeLab
In the same journal
PLoS ONE
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 73 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf