<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sakellariou, A.</style></author><author><style face="normal" font="default" size="100%">Sanoudou, D.</style></author><author><style face="normal" font="default" size="100%">Spyrou, G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigating the minimum required number of genes for the classification of neuromuscular disease microarray data</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans Inf Technol BiomedIEEE Trans Inf Technol BiomedIEEE Trans Inf Technol Biomed</style></secondary-title><alt-title><style face="normal" font="default" size="100%">IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society</style></alt-title><short-title><style face="normal" font="default" size="100%">IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology SocietyIEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology So</style></short-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">*Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational Biology/*methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Gene Expression Profiling/*methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular Diagnostic Techniques/*methods</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuromuscular Diseases/classification/*genetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Oligonucleotide Array Sequence Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">349-55</style></pages><isbn><style face="normal" font="default" size="100%">1558-0032 (Electronic)1089-7771 (Linking)</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The discovery of potential microarray markers, which will expedite molecular diagnosis/prognosis and provide reliable results to clinical decision-making and treatment selection for patients, is of paramount importance. Feature selection techniques, which aim at minimizing the dimensionality of the microarray data by keeping the most statistically significant genes, are a powerful approach toward this goal. In this paper, we investigate the minimum required subsets of genes, which best classify neuromuscular disease data. For this purpose, we implemented a methodology pipeline that facilitated the use of multiple feature selection methods and subsequent performance of data classification. Five feature selection methods on datasets from ten different neuromuscular diseases were utilized. Our findings reveal subsets of very small number of genes, which can successfully classify normal/disease samples. Interestingly, we observe that similar classification results may be obtained from different subsets of genes. The proposed methodology can expedite the identification of small gene subsets with high-classification accuracy that could ultimately be used in the genetics clinics for diagnostic, prognostic, and pharmacogenomic purposes.</style></abstract><accession-num><style face="normal" font="default" size="100%">21427026</style></accession-num><notes><style face="normal" font="default" size="100%">Sakellariou, ArgirisSanoudou, DespinaSpyrou, Georgeeng2011/03/24 06:00IEEE Trans Inf Technol Biomed. 2011 May;15(3):349-55. doi: 10.1109/TITB.2011.2130531. Epub 2011 Mar 22.</style></notes><auth-address><style face="normal" font="default" size="100%">Biomedical Research Foundation, Academy of Athens, and the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens 115 27, Greece. asake@bioacademy.gr</style></auth-address></record></records></xml>