ArticlePloS one2017
Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A joint complex network and machine learning approach for the identification of discriminative gene communities in autistic brain.PloS one · 2025Article
- Biomarker prediction in autism spectrum disorder using a network-based approach.BMC medical genomics · 2023Article
- A water quality assessment method based on an improved grey relational analysis and particle swarm optimization multi-classification support vector machine.Frontiers in plant science · 2023Article
- IoMT amid COVID-19 pandemic: Application, architecture, technology, and security.Journal of network and computer applications (Online) · 2021Review
- Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data.PloS one · 2021Article
- HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets.PloS one · 2021Article
- A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches.PeerJ. Computer science · 2021Article
- A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder.Brain sciences · 2020Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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