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논문 기본 정보

자료유형
학술저널
저자정보
Punacha Gagan (Nitte University Centre for Science Education) Adiga Rama (Nitte University Centre for Science Education)
저널정보
한국유전학회 Genes & Genomics Genes and Genomics Vol.46 No.3
발행연도
2024.3
수록면
341 - 354 (14page)
DOI
10.1007/s13258-023-01467-6

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Background With rise in variants of SARS-CoV-2, it is necessary to classify the emerging SARS-CoV-2 for early detection and thereby reduce human transmission. Genomic and proteomic information have less frequently been used for classifying in a machine learning (ML) approach for detection of SARS-CoV-2. Objective With this aim we used nucleoprotein and viral proteomic evolutionary information of SARS-CoV-2 along with the charge and basicity distribution of amino acids from various strains of SARS-CoV-2 to generate a disease severity model based on ML. Methods All sequence and clinical data were obtained from GISAID. Proteomic level calculations were added to comprise the dataset. The training set was used for feature selection. Select K- Best feature selection method was employed which was cross validated with testing set and performance evaluated. Delong’s test was also done. We also employed BIRCH clustering on SARS-CoV-2 for clustering the strains. Results Out of six ML models four were successful in training and testing. Extra Trees algorithm generated a micro-averaged F1-score of 74.2% and a weighted averaged area under the receiver operating characteristic curve (AUC-ROC) score of 73.7% with multi-class option. The feature selection set to 5, enhanced the ROC AUC from 73.7 to 76.4%. Accuracy of the selected model of 86.9% was achieved. Conclusion The unique features identified in the ML approach was able to classify disease severity into classes and had potential for predicting risk in newer variants.

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