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A global structural em algorithm for a model of cancer progression
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, Centres, Science for Life Laboratory, SciLifeLab.
2011 (English)In: Adv. Neural Inf. Process. Syst.: Annu. Conf. Neural Inf. Process. Syst., NIPS, 2011Conference paper, Published paper (Refereed)
Abstract [en]

Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein's path model of colon cancer was a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression, devising learning algorithms, and applying these to cross-sectional data. Beerenwinkel et al. provided, what they coined, EM-like algorithms for Oncogenetic Trees (OTs) and mixtures of such. Given the small size of current and future data sets, it is important to minimize the number of parameters of a model. For this reason, we too focus on tree-based models and introduce Hidden-variable Oncogenetic Trees (HOTs). In contrast to OTs, HOTs allow for errors in the data and thereby provide more realistic modeling. We also design global structural EM algorithms for learning HOTs and mixtures of HOTs (HOT-mixtures). The algorithms are global in the sense that, during the M-step, they find a structure that yields a global maximum of the expected complete log-likelihood rather than merely one that improves it. The algorithm for single HOTs performs very well on reasonable-sized data sets, while that for HOT-mixtures requires data sets of sizes obtainable only with tomorrow's more cost-efficient technologies.

Place, publisher, year, edition, pages
2011.
Series
Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011
Keyword [en]
Cancer progression, Cancer research, Colon cancer, Complex pattern, Cost-efficient, Data sets, EM algorithms, Global maximum, Log likelihood, Path models, Realistic modeling, Tree-based model, Forestry, Learning algorithms, Mathematical models, Maximum likelihood, Mixtures, Diseases, Algorithms
National Category
Medical Biotechnology
Identifiers
URN: urn:nbn:se:kth:diva-150611Scopus ID: 2-s2.0-84860609411ISBN: 9781618395993 (print)OAI: oai:DiVA.org:kth-150611DiVA: diva2:744842
Conference
25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011, 12 December 2011 through 14 December 2011, Granada
Funder
Swedish e‐Science Research CenterScience for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20140908

Available from: 2014-09-08 Created: 2014-09-08 Last updated: 2014-09-08Bibliographically approved

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CiteExportLink to record
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  • apa
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