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A Selection of an Optimal Framework Identifying the Prominent Autism Risk Gene Biomarkers from Gene Expression Data Using Neural Network

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Abstract

Autism is not just the consequence of mutations in single genes but across many genes. The dynamic changes in genes are well expressed by gene expression microarray data. The potential role of researchers still persists in finding the most prominent genes that influence autism risk. In this work, microarray data collected has 146 samples of autism and a control group with 54,613 gene expression features. The features and samples suffer from dimensionality issues to train the machine learning model. To reduce the dimension of features without loss of generality, four feature selection methods are used to select 100 prominent genes each with statistical significance. The feature selection model is concatenated to the neural network classifier model for classification. The neural network model is configured with proper adjustments of hyper parameters. Each feature selection method is combined with a neural network model for the analysis of prominent genes to better the classification of autism. The metrics used to access the classification are accuracy and loss. The neural network model is trained for ten folds and tested for untrained data. The optimized framework shows an accuracy of 78.6% with a minimum loss of 0.694 for the untrained data. The optimized model is identified with a trade-off between accuracy and loss.

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Authors and Affiliations

  1. Department of Electronics and Communication Enginerring, D.B.I.T, Affiliated to Visvesvaraya Technological University, Bengaluru, Karnataka, 74, India

    B. S. Roopa

  2. Department of Electronics and Communication Engineering, D.S.A.T.M, Affiliated to Visvesvaraya Technological University, Bengaluru, Karnataka, 82, India

    R. Manjunatha Prasad

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  1. B. S. Roopa
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  2. R. Manjunatha Prasad
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Correspondence to B. S. Roopa.

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This article is part of the topical collection “Data Science and Communication” guest edited by Kamesh Namudri, Naveen Chilamkurti, Sushma S J and S. Padmashree.

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Roopa, B.S., Manjunatha Prasad, R. A Selection of an Optimal Framework Identifying the Prominent Autism Risk Gene Biomarkers from Gene Expression Data Using Neural Network. SN COMPUT. SCI. 2, 241 (2021). https://doi.org/10.1007/s42979-021-00559-y

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