An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

作     者：G.Anurekha P.Geetha 

作者机构：Department of Information Science and TechnologyCollege of EngineeringAnna UniversityChennai600025India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第3期

页      面：3049-3064页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

主　　题：Autism spectrum disorder feature selection ensemble gene selection microarray gene expression deep neural network meta-heuristic 

摘      要：Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in *** microarray data is used as a diagnostic tool to identify the genetics of the ***,microarray data is large and has a high ***,it suffers from the problem of *** microarray data,the sample size and variance of the gene expression will lead to overfitting and misclassifi*** the autism gene(feature)subset from microarray data is an important and challenging research *** has to be efficiently addressed to improve gene feature selection and classifi*** overcome the challenges,a novel Intelligent Hybrid Ensem-ble Gene Selection(IHEGS)model is proposed in this *** proposed model integrates the intelligence of different feature selection techniques over the data *** this model,the initial gene selection is carried out by data perturba-tion,and thefinal autism gene subset is obtained by functional perturbation,which reduces the problem of dimensionality in microarray *** functional perturbation module employs three meta-heuristic swarm intelligence-based tech-niques for gene *** obtained gene subset is validated by the Deep Neural Network(DNN)*** proposed model is implemented using python with six National Center for Biotechnology Information(NCBI)gene expression *** the comparative study with other existing state-of-the-art systems,the proposed model provides stable results in terms of feature selection and clas-sification accuracy.