Optimal biomarker selection using adaptive Social Ski-Driver optimization for liver cancer detection

• Autorzy: Gunasekhar P. , Vijayalakshmi S.

Identyfikatory

DOI

10.1016/j.bbe.2020.10.005

• Języki publikacji: EN

Abstrakty

EN

Most essential biomolecule found in the human body is a biomarker; with these biomarkers, the abnormal biological processes and disease states of each patient can be accurately determined. Nowadays, the biomarker applications are frequently applied during clinical trials to identify cancer patients. In this method, the major significance of miRNA biomarkers during liver cancer detection is analysed. For such analysis, a deep learning technique is introduced along with optimization algorithms. Six different filter-based approaches are considered for feature selection they are Chi-Squared (Chi2), Information Gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), RelieF (RF) and RF-W. Two high ranked features from these selected features are extracted by the Modified Social Ski-Driver optimization (MSSO) algorithm. With that high ranked features, the liver cancer tissues are accurately detected by Sunflower Optimization-based deep neural network (DSFNN) approach. The analysis part concludes that a miRNA biomarker having a higher rank provide better cancer detection results than other low-ranked biomarkers. In this work, 10 different, clinically verified miRNA biomarkers are selected for this detection process. The data required for liver cancer detection is selected from NCBI-GEO database. The performance of this entire cancer detection process is evaluated by accuracy, sensitivity, precision, specificity, and Area under curve (AUC) metrics. Furthermore, we also determined that the usage of 10, 5, and 3 clinically verified miRNAs provide better cancer detection results than other miRNAs. Among all clinically verified miRNAs, the selected three biomarkers (hsa-mir-10b, hsa-let-7c, hsa-mir- 145) has attained higher recognition result. The performance result attained by the proposed DSFNN is compared with five different algorithms for both training and validation datasets.

Słowa kluczowe

EN

deep neural network; feature selection; liver cancer detection; miRNA biomarker; modified social ski-driver; sunflower optimization

PL

sieć neuronowa głęboka; selekcja cech; rak wątroby

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 4
• Strony: 1611--1625
• Opis fizyczny: Bibliogr. 42 poz., rys., tab., wykr.

Twórcy

autor

Gunasekhar P.

• R.M.K. Engineering College, Anna University, R.M.S Nagar, Kavaraipettai, 601206, Gummidipoondi-Taluk, Thiruvallur, India

autor

Vijayalakshmi S.

• R.M.K. Engineering College, Anna University, R.M.S Nagar, Kavaraipettai, Gummidipoondi-Taluk, Thiruvallur, India

Bibliografia

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