Healthcare Biclustering-Based Prediction on Gene Expression Dataset.
In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and re...
| Publicado en: | BioMed Research International pp. 1 - 8 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2/28/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=155494555&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 155494555 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2/28/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 155494555 155494555 155494555 10.1155/2022/2263194 155494555 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Healthcare Biclustering-Based Prediction on Gene Expression Dataset. aug: au: Ramkumar, M. Basker, N. Pradeep, D. Prajapati, Ramesh Yuvaraj, N. Arshath Raja, R. Suresh, C. Vignesh, Rahul Barakkath Nisha, U. Srihari, K. Alene, Assefa affil: Department of Computer Science and Engineering, HKBK College of Engineering, India sug: subj: Gene Expression Cluster Analysis Algorithms Machine Learning Human Prediction Models Bioinformatics Data Mining ab: In this paper, we develop a healthcare biclustering model in the field of healthcare to reduce the inconveniences linked to the data clustering on gene expression. The present study uses two separate healthcare biclustering approaches to identify specific gene activity in certain environments and remove the duplication of broad gene information components. Moreover, because of its adequacy in the problem where populations of potential solutions allow exploration of a greater portion of the research area, machine learning or heuristic algorithm has become extensively used for healthcare biclustering in the field of healthcare. The study is evaluated in terms of average match score for nonoverlapping modules, overlapping modules through the influence of noise for constant bicluster and additive bicluster, and the run time. The results show that proposed FCM blustering method has higher average match score, and reduced run time proposed FCM than the existing PSO-SA and fuzzy logic healthcare biclustering methods. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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