Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm

• Published in: PloS one Vol. 12; no. 11; p. e0187371
• Main Authors: Hameed, Shilan S., Hassan, Rohayanti, Muhammad, Fahmi F.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.11.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial intelligence; Autism; Autistic Disorder - genetics; Bioinformatics; Biology and Life Sciences; Biomarkers; Cancer; Classification; Computer and Information Sciences; Data mining; Deoxyribonucleic acid; Diagnosis; DNA; Filters; Gene Expression; Genes; Genetic aspects; Genomes; Humans; Informatics; Medicine and Health Sciences; Methods; Neural networks; Neurosciences; Optimization; Optimization theory; Particle swarm optimization; Physical Sciences; Proteins; Research and Analysis Methods; Set theory; Social Sciences; Statistical analysis; Statistics; Support Vector Machine; Support vector machines; Swarm intelligence
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0187371

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.