Bio-marker identification for COVID-19 remains a vital research area to improve current and future pandemic responses. Innovative artificial intelligence and machine learning-based systems may leverage the large quantity and complexity of single cell sequencing data to quickly identify disease with high sensitivity. In this study, we developed a novel approach to classify patient COVID-19 infection severity using single-cell sequencing data derived from patient BronchoAlveolar Lavage Fluid (BALF) samples. We also identified key genetic biomarkers associated with COVID-19 infection severity. Feature importance scores from high performing COVID-19 classifiers were used to identify a set of novel genetic biomarkers that are predictive of COVID-19 infection severity. Treatment development and pandemic reaction may be greatly improved using our novel big-data approach. Our implementation is available on https://github.com/aekanshgoel/COVID-19_scRNAseq.

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Cited By

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Li XZhang CChen WShi WWang M(2023)Identification of Single-Cell RNA Sequencing Molecular Signatures for COVID-19 Infection Severity Classification2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385934(2044-2047)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385934
Zamitalo AXie QAllam MPhilip PShi WGiuste FMarteau BMurakoso MWang M(2022)Development of Machine Learning Regression Model for COVID-19 Drug Target Prediction2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995319(2808-2815)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995319

Index Terms

Identification of COVID-19 severity and associated genetic biomarkers based on scRNA-seq data
1. Applied computing
  1. Life and medical sciences

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BCB '22: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 2022

549 pages

ISBN:9781450393867

DOI:10.1145/3535508

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

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Published: 07 August 2022

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BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 7 - 10, 2022

Illinois, Northbrook

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Li XZhang CChen WShi WWang M(2023)Identification of Single-Cell RNA Sequencing Molecular Signatures for COVID-19 Infection Severity Classification2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM58861.2023.10385934(2044-2047)Online publication date: 5-Dec-2023
https://doi.org/10.1109/BIBM58861.2023.10385934
Zamitalo AXie QAllam MPhilip PShi WGiuste FMarteau BMurakoso MWang M(2022)Development of Machine Learning Regression Model for COVID-19 Drug Target Prediction2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM55620.2022.9995319(2808-2815)Online publication date: 6-Dec-2022
https://doi.org/10.1109/BIBM55620.2022.9995319

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