New Probabilistic network models and algorithms for oncogenesis
2006 (English)In: Journal of Computational Biology, ISSN 1066-5277, E-ISSN 1557-8666, Vol. 13, no 4, 853-865 p.Article in journal (Refereed) Published
Chromosomal aberrations in solid tumors appear in complex patterns. It is important to understand how these patterns develop, the dynamics of the process, the temporal or even causal order between aberrations, and the involved pathways. Here we present network models for chromosomal aberrations and algorithms for training models based on observed data. Our models are generative probabilistic models that can be used to study dynamical aspects of chromosomal evolution in cancer cells. They are well suited for a graphical representation that conveys the pathways found in a dataset. By allowing only pairwise dependencies and partition aberrations into modules, in which all aberrations are restricted to have the same dependencies, we reduce the number of parameters so that datasets sizes relevant to cancer applications can be handled. We apply our framework to a dataset of colorectal cancer tumor karyotypes. The obtained model explains the data significantly better than a model where independence between the aberrations is assumed. In fact, the obtained model performs very well with respect to several measures of goodness of fit and is, with respect to repetition of the training, more or less unique.
Place, publisher, year, edition, pages
2006. Vol. 13, no 4, 853-865 p.
cancer, chromosomal aberration, probabilistic model, learning algorithm, graphical representation, chromosome-abnormalities, tree models, pathways, cancer, tumorigenesis, deletion, distinct, tumors
IdentifiersURN: urn:nbn:se:kth:diva-15767ISI: 000238488000001ScopusID: 2-s2.0-33745296683OAI: oai:DiVA.org:kth-15767DiVA: diva2:333809
QC 201005252010-08-052010-08-05Bibliographically approved