{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:37:10Z","timestamp":1762324630517,"version":"3.37.3"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2020,1,23]],"date-time":"2020-01-23T00:00:00Z","timestamp":1579737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100019081","name":"Hunan Provincial Science and Technology Project Foundation","doi-asserted-by":"crossref","award":["2018TP1018"],"award-info":[{"award-number":["2018TP1018"]}],"id":[{"id":"10.13039\/501100019081","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61502167"],"award-info":[{"award-number":["61502167"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007333","name":"ADNI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007333","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The multimodal data fusion analysis becomes another important field for brain disease detection and increasing researches concentrate on using neural network algorithms to solve a range of problems. However, most current neural network optimizing strategies focus on internal nodes or hidden layer numbers, while ignoring the advantages of external optimization. Additionally, in the multimodal data fusion analysis of brain science, the problems of small sample size and high-dimensional data are often encountered due to the difficulty of data collection and the specialization of brain science data, which may result in the lower generalization performance of neural network.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a genetically evolved random neural network cluster (GERNNC) model. Specifically, the fusion characteristics are first constructed to be taken as the input and the best type of neural network is selected as the base classifier to form the initial random neural network cluster. Second, the cluster is adaptively genetically evolved. Based on the GERNNC model, we further construct a multi-tasking framework for the classification of patients with brain disease and the extraction of significant characteristics. In a study of genetic data and functional magnetic resonance imaging data from the Alzheimer\u2019s Disease Neuroimaging Initiative, the framework exhibits great classification performance and strong morbigenous factor detection ability. This work demonstrates that how to effectively detect pathogenic components of the brain disease on the high-dimensional medical data and small samples.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The Matlab code is available at https:\/\/github.com\/lizi1234560\/GERNNC.git.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz967","type":"journal-article","created":{"date-parts":[[2020,1,18]],"date-time":"2020-01-18T12:09:42Z","timestamp":1579349382000},"page":"2561-2568","source":"Crossref","is-referenced-by-count":35,"title":["Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2715-3360","authenticated-orcid":false,"given":"Xia-an","family":"Bi","sequence":"first","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing"},{"name":"College of Information Science and Engineering, Hunan Normal University , Changsha, China"}]},{"given":"Yingchao","family":"Liu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing"},{"name":"College of Information Science and Engineering, Hunan Normal University , Changsha, China"}]},{"given":"Yiming","family":"Xie","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing"},{"name":"College of Information Science and Engineering, Hunan Normal University , Changsha, China"}]},{"given":"Xi","family":"Hu","sequence":"additional","affiliation":[{"name":"Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing"},{"name":"College of Information Science and Engineering, Hunan Normal University , Changsha, China"}]},{"given":"Qinghua","family":"Jiang","sequence":"additional","affiliation":[{"name":"Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology , Harbin, China"}]}],"member":"286","published-online":{"date-parts":[[2020,1,23]]},"reference":[{"key":"2023013110264983400_btz967-B1","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.neuroimage.2018.03.016","article-title":"Applying dimension reduction to EEG data by principal component analysis reduces the quality of its subsequent independent component decomposition","volume":"175","author":"Artoni","year":"2018","journal-title":"Neuroimage"},{"key":"2023013110264983400_btz967-B2","doi-asserted-by":"crossref","first-page":"5","DOI":"10.2174\/1871527314666161124115531","article-title":"A feature-free 30-disease pathological brain detection system by linear regression classifier","volume":"16","author":"Chen","year":"2017","journal-title":"CNS Neurol. 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