Predicting COVID-19 Severity Integrating RNA-Seq Data Using Machine Learning Techniques
Current Bioinformatics
; 18(3):221-231, 2023.
Artículo
en Inglés
| EMBASE | ID: covidwho-2312823
ABSTRACT
A fundamental challenge in the fight against COVID-19 is the development of reliable and accurate tools to predict disease progression in a patient. This information can be extremely useful in distinguishing hospitalized patients at higher risk for needing UCI from patients with low severity. How SARS-CoV-2 infection will evolve is still unclear. Method(s) A novel pipeline was developed that can integrate RNA-Seq data from different databases to obtain a genetic biomarker COVID-19 severity index using an artificial intelligence algorithm. Our pipeline ensures robustness through multiple cross-validation processes in different steps. Result(s) CD93, RPS24, PSCA, and CD300E were identified as COVID-19 severity gene signatures. Furthermore, using the obtained gene signature, an effective multi-class classifier capable of discrimi-nating between control, outpatient, inpatient, and ICU COVID-19 patients was optimized, achieving an accuracy of 97.5%. Conclusion(s) In summary, during this research, a new intelligent pipeline was implemented to develop a specific gene signature that can detect the severity of patients suffering COVID-19. Our approach to clinical decision support systems achieved excellent results, even when processing unseen samples. Our system can be of great clinical utility for the strategy of planning, organizing and managing human and material resources, as well as for automatically classifying the severity of patients affected by COVID-19.Copyright © 2023 Bentham Science Publishers.
cdss; covid-19; feature selection; gene expression; machine learning; severity; adult; algorithm; article; artificial intelligence; classifier; clinical decision support system; controlled study; coronavirus disease 2019; COVID-19 Severity score; cross validation; genetic marker; hospital patient; human; outpatient; pipeline; RNA sequencing; validation process; biological marker; endogenous compound; prostate stem cell antigen
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
EMBASE
Tipo de estudio:
Estudio pronóstico
Idioma:
Inglés
Revista:
Current Bioinformatics
Año:
2023
Tipo del documento:
Artículo
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