Gene selection from large-scale gene expression data based on fuzzy interactive multi-objective binary optimization for medical diagnosis

• Autorzy: Shahbeig S. , Rahideh A. , Helfroush M. S. , Kazemi K.

Identyfikatory

DOI

10.1016/j.bbe.2018.02.002

• Języki publikacji: EN

Abstrakty

EN

An efficient fuzzy interactive multi-objective optimization method is proposed to select the sub-optimal subset of genes from large-scale gene expression data. It is based on the binary particle swarm optimization (BPSO) algorithm tuned by a chaotic method. The proposed method is able to select the sub-optimal subset of genes with the least number of features that can accurately distinguish between the two classes, e.g. the normal and cancerous samples. The proposed method is evaluated on several publicly available microarray and RNA-sequencing gene expression datasets such as leukemia, colon cancer, central nervous system, lung cancer, ovarian cancer, prostate cancer and RNA-seq lung disease. The results indicate that the proposed method can identify the minimum number of genes to achieve the most accuracy, sensitivity and specificity in the classification process. Achieving 100% accuracy in six out of the seven datasets investigated in this study, demonstrates the high capacity of the proposed algorithm to find the sub-optimal subset of genes. This approach is useful in clinical applications to extract the most influential genes on a disease and to find the treatment procedure for the disease.

Słowa kluczowe

EN

fuzzy interactive method; suboptimal subset; gene selection; large-scale data; multiobjective optimization; adaptive binary particle swarm optimization

PL

podzbiór suboptymalny; selekcja genów; dane wielkoskalowe; optymalizacja wielokryterialna

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 2
• Strony: 313--328
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Shahbeig S.

• Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

autor

Rahideh A.

• Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

autor

Helfroush M. S.

• Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

autor

Kazemi K.

• Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).