Identification of covid-19 severity and associated genetic biomarkers based on scrna-SEQ data
13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2029544
ABSTRACT
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. © 2022 Owner/Author.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022
Year:
2022
Document Type:
Article
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