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Chong HY, Yap HJ, Tan SC, Yap KS, Wong SY. Advances of metaheuristic algorithms in training neural networks for industrial applications. Soft comput 2021. [DOI: 10.1007/s00500-021-05886-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Kocabey Çiftçi P, Unutmaz Durmuşoğlu ZD. A multi-stage learning-based fuzzy cognitive maps for tobacco use. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-04860-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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An extensive review of computational intelligence-based optimization algorithms: trends and applications. Soft comput 2020. [DOI: 10.1007/s00500-020-04958-w] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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A novel parallel object-tracking behavior algorithm based on dynamics for data clustering. Soft comput 2020. [DOI: 10.1007/s00500-019-04058-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Goel L, Raman S, Dora SS, Bhutani A, Aditya AS, Mehta A. Hybrid computational intelligence algorithms and their applications to detect food quality. Artif Intell Rev 2019. [DOI: 10.1007/s10462-019-09705-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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Oyelade J, Isewon I, Oladipupo F, Aromolaran O, Uwoghiren E, Ameh F, Achas M, Adebiyi E. Clustering Algorithms: Their Application to Gene Expression Data. Bioinform Biol Insights 2016; 10:237-253. [PMID: 27932867 PMCID: PMC5135122 DOI: 10.4137/bbi.s38316] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 09/05/2016] [Accepted: 09/09/2016] [Indexed: 12/17/2022] Open
Abstract
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.
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Affiliation(s)
- Jelili Oyelade
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
| | - Funke Oladipupo
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
| | - Olufemi Aromolaran
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
| | - Efosa Uwoghiren
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
| | - Faridah Ameh
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
| | - Moses Achas
- Department of Computer Science and Information Technology, Bells University of Technology, Ota, Ogun State, Nigeria
| | - Ezekiel Adebiyi
- Department of Computer and Information Sciences, Covenant University, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, Nigeria
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Razavi SF, Sajedi H. Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2015. [DOI: 10.3233/ifs-151700] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Seyedeh Fatemeh Razavi
- Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Hedieh Sajedi
- Department of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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A New Algorithm for Data Clustering Based on Cuckoo Search Optimization. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2014. [DOI: 10.1007/978-3-319-01796-9_6] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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