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- W4385317046 abstract "Disease identification from gene expression microarray data is an extremely important research problem. Analysis of microarray data helps to accurately diagnose the disease types, finds therapeutic plans for that disease, helps to gain insight into molecular mechanisms of that genetic disease and many more. In this work, a filter-wrapper-based disease identification technique inspired by gray wolf optimization (GWO) algorithm is proposed. In the first stage, mutual information-based filter method is used to discard irrelevant genes. Then, the rest of the genes are passed to GWO embedded with support vector machine to detect the most relevant genes and corresponding disease types. Nearly 100% accuracy is obtained with an average of 3.9 genes on three microarray datasets. On another dataset, 91.66% accuracy is obtained with an average of 4.6 genes only. The comparative results with other state-of-the-art methods confirm the superiority of this proposed method." @default.
- W4385317046 created "2023-07-28" @default.
- W4385317046 creator A5049120490 @default.
- W4385317046 creator A5070865508 @default.
- W4385317046 date "2023-01-01" @default.
- W4385317046 modified "2023-09-26" @default.
- W4385317046 title "A Gray Wolf Optimization-Inspired Hybrid Method for Disease Identification" @default.
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- W4385317046 doi "https://doi.org/10.1007/978-981-99-2710-4_2" @default.
- W4385317046 hasPublicationYear "2023" @default.
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