Modeling Cancer Progression via Pathway Dependencies Export

@article{citeulike:2410427, abstract = {Author Summary Cancer is a complex disease with many subtypes that differ substantially with respect to their onset, progression, and response to treatment. Better understanding of the etiology and mechanism of cancer should help improve the diagnosis, prognosis, and treatment of cancer that will kill more than half a million Americans this year alone. Our study illustrates how integration of data over multiple stages and modeling tumorigenesis at the level of regulatory pathways or sets of genes provide robust and interpretable novel hypotheses concerning root genetic causes responsible for cancer initiation, progression, and invasion. Our modeling approach is one of the first approaches that combines multiple microarray datasets in a truly integrative framework that promotes the interpretability of important factors or pathways in one or more datasets. We apply this analysis of tumor progression to both prostate cancer and melanoma to provide information that can lead to the identification of novel biomarkers and give a basis for how genetic disruptions serve to alter actions in specific cell types.}, author = {Edelman, Elena J. and Guinney, Justin and Chi, Jen-Tsan and Febbo, Phillip G. and Mukherjee, Sayan}, citeulike-article-id = {2410427}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.0040028}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18282083}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18282083}, day = {15}, doi = {10.1371/journal.pcbi.0040028}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {cancer, dependence, diagnosis, etiology, gastric-cancer-group, integrative, interpretation, mechanism, microarray, modelling, pathway, prognosis, progression, treatment}, month = {February}, number = {2}, pages = {e28+}, posted-at = {2009-10-04 00:30:01}, priority = {3}, publisher = {Public Library of Science}, title = {Modeling Cancer Progression via Pathway Dependencies}, url = {http://dx.doi.org/10.1371/journal.pcbi.0040028}, volume = {4}, year = {2008} }

TY - JOUR ID - citeulike:2410427 L3 - citeulike-article-id:2410427 N2 - Author Summary Cancer is a complex disease with many subtypes that differ substantially with respect to their onset, progression, and response to treatment. Better understanding of the etiology and mechanism of cancer should help improve the diagnosis, prognosis, and treatment of cancer that will kill more than half a million Americans this year alone. Our study illustrates how integration of data over multiple stages and modeling tumorigenesis at the level of regulatory pathways or sets of genes provide robust and interpretable novel hypotheses concerning root genetic causes responsible for cancer initiation, progression, and invasion. Our modeling approach is one of the first approaches that combines multiple microarray datasets in a truly integrative framework that promotes the interpretability of important factors or pathways in one or more datasets. We apply this analysis of tumor progression to both prostate cancer and melanoma to provide information that can lead to the identification of novel biomarkers and give a basis for how genetic disruptions serve to alter actions in specific cell types. IS - 2 JF - PLoS Comput Biol SN - 1553-7358 TI - Modeling Cancer Progression via Pathway Dependencies PB - Public Library of Science VL - 4 SP - e28 KW - cancer KW - dependence KW - diagnosis KW - etiology KW - gastric-cancer-group KW - integrative KW - interpretation KW - mechanism KW - microarray KW - modelling KW - pathway KW - prognosis KW - progression KW - treatment AU - Edelman, Elena AU - Guinney, Justin AU - Chi, Jen-Tsan AU - Febbo, Phillip AU - Mukherjee, Sayan PY - 2008/02/15/ UR - http://dx.doi.org/10.1371/journal.pcbi.0040028 ER -