New Probabilistic network models and algorithms for oncogenesis
(2006) In Journal of Computational Biology 13(4). p.853-865- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/404896
- author
- Hjelm, M ; Höglund, Mattias LU and Lagergren, J
- organization
- publishing date
- 2006
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- algorithm, learning, probabilistic model, cancer, chromosomal aberration, graphical representation
- in
- Journal of Computational Biology
- volume
- 13
- issue
- 4
- pages
- 853 - 865
- publisher
- Mary Ann Liebert, Inc.
- external identifiers
-
- wos:000238488000001
- pmid:16761915
- scopus:33745296683
- pmid:16761915
- ISSN
- 1557-8666
- DOI
- 10.1089/cmb.2006.13.853
- language
- English
- LU publication?
- yes
- id
- 266ef0b3-6a81-47f5-862d-40649e4c2171 (old id 404896)
- date added to LUP
- 2016-04-01 11:35:17
- date last changed
- 2022-01-26 07:20:40
@article{266ef0b3-6a81-47f5-862d-40649e4c2171,
abstract = {{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.}},
author = {{Hjelm, M and Höglund, Mattias and Lagergren, J}},
issn = {{1557-8666}},
keywords = {{algorithm; learning; probabilistic model; cancer; chromosomal aberration; graphical representation}},
language = {{eng}},
number = {{4}},
pages = {{853--865}},
publisher = {{Mary Ann Liebert, Inc.}},
series = {{Journal of Computational Biology}},
title = {{New Probabilistic network models and algorithms for oncogenesis}},
url = {{http://dx.doi.org/10.1089/cmb.2006.13.853}},
doi = {{10.1089/cmb.2006.13.853}},
volume = {{13}},
year = {{2006}},
}