2017 |
Hameed, S S; Hassan, R; Muhammad, F F Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm Journal Article PLoS ONE, 12 (11), 2017, ISSN: 19326203, (cited By 11). Abstract | Links | BibTeX | Tags: Accuracy, Algorithms, Article, Autism, Autism Spectrum Disorders, CAPS2 Gene, Classification (of information), Classifier, Experimental Study, Gene, Gene Expression, Gene Identification, Genetic Association, Genetic Procedures, Genetic Risk, Genetics, Geometric Binary Particle Swarm Optimization Support Vector Machine Algorithm, Human, RIsk Assessment, Standardization, Statistical Filter, Statistical Parameters, Statistics, Support Vector Machines @article{Hameed2017, title = {Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm}, author = {S S Hameed and R Hassan and F F Muhammad}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033361187&doi=10.1371%2fjournal.pone.0187371&partnerID=40&md5=f9260d41165145f229a3cf157699635e}, doi = {10.1371/journal.pone.0187371}, issn = {19326203}, year = {2017}, date = {2017-01-01}, journal = {PLoS ONE}, volume = {12}, number = {11}, publisher = {Public Library of Science}, abstract = {In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy. © 2017 Hameed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.}, note = {cited By 11}, keywords = {Accuracy, Algorithms, Article, Autism, Autism Spectrum Disorders, CAPS2 Gene, Classification (of information), Classifier, Experimental Study, Gene, Gene Expression, Gene Identification, Genetic Association, Genetic Procedures, Genetic Risk, Genetics, Geometric Binary Particle Swarm Optimization Support Vector Machine Algorithm, Human, RIsk Assessment, Standardization, Statistical Filter, Statistical Parameters, Statistics, Support Vector Machines}, pubstate = {published}, tppubtype = {article} } In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy. © 2017 Hameed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
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2017 |
Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm Journal Article PLoS ONE, 12 (11), 2017, ISSN: 19326203, (cited By 11). |