Biomarker identification by feature wrappers. Export

@article{citeulike:530848, abstract = {Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expression data are not efficient for biomarker identification and disease diagnosis. In this paper, we propose a general framework to incorporate feature (gene) selection into pattern recognition in the process to identify biomarkers. Using this framework, we develop three feature wrappers that search through the space of feature subsets using the classification error as measure of goodness for a particular feature subset being "wrapped around": linear discriminant analysis, logistic regression, and support vector machines. To effectively carry out this computationally intensive search process, we employ sequential forward search and sequential forward floating search algorithms. To evaluate the performance of feature selection for biomarker identification we have applied the proposed methods to three data sets. The preliminary results demonstrate that very high classification accuracy can be attained by identified composite classifiers with several biomarkers.}, address = {Human Genetics Center, University of Texas-Houston, Houston, TX 77225, USA. mxiong@utsph.sph.uth.tmc.edu}, author = {Xiong, M. and Fang, X. and Zhao, J.}, citeulike-article-id = {530848}, citeulike-linkout-0 = {http://dx.doi.org/10.1101/gr.190001}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/11691853}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=11691853}, doi = {10.1101/gr.190001}, issn = {1088-9051}, journal = {Genome research}, keywords = {array, biomarker, cluster, feature\_selection}, month = {November}, number = {11}, pages = {1878--1887}, posted-at = {2009-11-10 06:23:43}, priority = {2}, title = {Biomarker identification by feature wrappers.}, url = {http://dx.doi.org/10.1101/gr.190001}, volume = {11}, year = {2001} }

TY - JOUR ID - citeulike:530848 L3 - citeulike-article-id:530848 N2 - Gene expression studies bridge the gap between DNA information and trait information by dissecting biochemical pathways into intermediate components between genotype and phenotype. These studies open new avenues for identifying complex disease genes and biomarkers for disease diagnosis and for assessing drug efficacy and toxicity. However, the majority of analytical methods applied to gene expression data are not efficient for biomarker identification and disease diagnosis. In this paper, we propose a general framework to incorporate feature (gene) selection into pattern recognition in the process to identify biomarkers. Using this framework, we develop three feature wrappers that search through the space of feature subsets using the classification error as measure of goodness for a particular feature subset being "wrapped around": linear discriminant analysis, logistic regression, and support vector machines. To effectively carry out this computationally intensive search process, we employ sequential forward search and sequential forward floating search algorithms. To evaluate the performance of feature selection for biomarker identification we have applied the proposed methods to three data sets. The preliminary results demonstrate that very high classification accuracy can be attained by identified composite classifiers with several biomarkers. IS - 11 JF - Genome research SN - 1088-9051 AD - Human Genetics Center, University of Texas-Houston, Houston, TX 77225, USA. mxiong@utsph.sph.uth.tmc.edu EP - 1887 TI - Biomarker identification by feature wrappers. VL - 11 SP - 1878 KW - array KW - biomarker KW - cluster KW - feature_selection AU - Xiong, M AU - Fang, X AU - Zhao, J PY - 2001/11// UR - http://dx.doi.org/10.1101/gr.190001 ER -