Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer

@article{citeulike:1439760, abstract = {Motivation: Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed. Results: We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others. Availability: the source code in Matlab is accessible via http://www.ee.cityu.edu.hk/\~{}twschow/category\_selection/category\_selection.htm Contact: ifkorf@ucdavis.edu Supplementary information: http://www.ee.cityu.edu.hk/\~{}twschow/category\_selection/category\_selection.htm 10.1093/bioinformatics/btm141}, author = {Huang, D. and Chow, Tommy W. }, citeulike-article-id = {1439760}, doi = {http://dx.doi.org/10.1093/bioinformatics/btm141}, journal = {Bioinformatics}, keywords = {cancer, gene\_list\_analysis, human}, month = {June}, number = {12}, pages = {1503--1510}, posted-at = {2007-07-06 17:56:39}, priority = {2}, title = {Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer}, url = {http://dx.doi.org/10.1093/bioinformatics/btm141}, volume = {23}, year = {2007} }

TY - JOUR ID - citeulike:1439760 L3 - citeulike-article-id:1439760 TI - Identifying the biologically relevant gene categories based on gene expression and biological data: an example on prostate cancer JF - Bioinformatics VL - 23 IS - 12 SP - 1503 EP - 1510 N2 - Motivation: Most gene-expression based studies aim to identify genes with the capability of distinguishing different phenotypes. Although analysis at the genomic level is important, results of the molecular/cellular level are essential for understanding biological mechanisms. To deliver molecular/cellular-level results, a two-stage scheme is widely employed. This scheme just evaluates biological processes/molecular activities individually, totally overlooking the relationship between processes/activities. This treatment conflicts with the fact that most biological processes/molecular activities do not work alone. In order to deliver improved results, this shortcoming should be addressed. Results: We design a selection model from a novel perspective to directly detect important gene functional categories (each category represents a cellular process or a molecular activity). More importantly, the correlations between gene categories are considered. Contributed by this capability, the proposed method shows its advantages over others. Availability: the source code in Matlab is accessible via http://www.ee.cityu.edu.hk/~twschow/category_selection/category_selection.htm Contact: ifkorf@ucdavis.edu Supplementary information: http://www.ee.cityu.edu.hk/~twschow/category_selection/category_selection.htm 10.1093/bioinformatics/btm141 KW - cancer KW - gene_list_analysis KW - human AU - Huang, D AU - Chow, Tommy PY - 2007/06/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btm141 ER -