Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm.
Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 19 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
1/9/2024
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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=174877259&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 174877259 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 1/9/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 174877259 174877259 174877259 10.1007/s10916-023-02031-1 174877259 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm. aug: au: Yaqoob, Abrar Verma, Navneet Kumar Aziz, Rabia Musheer affil: https://ror.org/02ax13658 School of Advanced Sciences and Languages, VIT Bhopal University, 466114, Kothrikalan, Sehore, India sug: subj: Gene Expression Neoplasms Classification Algorithms Machine Learning Support Vector Machine Birds Technology Utilization Gene Expression Profiling Confounding Variable Information Resources Information Science Methods Models, Theoretical Quality Improvement ab: Gene expression datasets offer a wide range of information about various biological processes. However, it is difficult to find the important genes among the high-dimensional biological data due to the existence of redundant and unimportant ones. Numerous Feature Selection (FS) techniques have been created to get beyond this obstacle. Improving the efficacy and precision of FS methodologies is crucial in order to identify significant genes amongst complicated complex biological data. In this work, we present a novel approach to gene selection called the Sine Cosine and Cuckoo Search Algorithm (SCACSA). This hybrid method is designed to work with well-known machine learning classifiers Support Vector Machine (SVM). Using a dataset on breast cancer, the hybrid gene selection algorithm's performance is carefully assessed and compared to other feature selection methods. To improve the quality of the feature set, we use minimum Redundancy Maximum Relevance (mRMR) as a filtering strategy in the first step. The hybrid SCACSA method is then used to enhance and optimize the gene selection procedure. Lastly, we classify the dataset according to the chosen genes by using the SVM classifier. Given the pivotal role gene selection plays in unraveling complex biological datasets, SCACSA stands out as an invaluable tool for the classification of cancer datasets. The findings help medical practitioners make well-informed decisions about cancer diagnosis and provide them with a valuable tool for navigating the complex world of gene expression data. pubtype: Academic Journal doctype: algorithm equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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