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Feature selection for effective prediction of SARS-COV-2 using machine learning

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Abstract

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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Availability of data and materials

There will be data transparency for the present research work. All downloaded data are available in public databases for which due acknowledgement and citations have been provided.

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Acknowledgements

The author wishes to thank Dr. Anirban Chakraborthy, Director, Nitte University Centre for Science Education and Research (NUCSER), and the Management of Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India for the support in establishing the center, providing facilities and continuous encouragement in research, including the present work.

Funding

This research received no external funding. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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  1. Nitte (Deemed to be University), Department of Molecular Genetics & Cancer, Nitte University Centre for Science Education & Research (NUCSER), Mangalore, Karnataka, India

    Gagan Punacha & Rama Adiga

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  1. Gagan Punacha
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Contributions

The first draft of the manuscript and primary analysis was performed by Gagan Punacha. Rama Adiga performed all other analysis. Both the authors commented on previous versions of the manuscript and read and approved the final manuscript.

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Correspondence to Rama Adiga.

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Punacha, G., Adiga, R. Feature selection for effective prediction of SARS-COV-2 using machine learning. Genes Genom 46, 341–354 (2024). https://doi.org/10.1007/s13258-023-01467-6

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