Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm.
J Cell Mol Med
; 28(4): e18105, 2024 02.
Article
en En
| MEDLINE
| ID: mdl-38339761
ABSTRACT
Complement inhibition has shown promise in various disorders, including COVID-19. A prediction tool including complement genetic variants is vital. This study aims to identify crucial complement-related variants and determine an optimal pattern for accurate disease outcome prediction. Genetic data from 204 COVID-19 patients hospitalized between April 2020 and April 2021 at three referral centres were analysed using an artificial intelligence-based algorithm to predict disease outcome (ICU vs. non-ICU admission). A recently introduced alpha-index identified the 30 most predictive genetic variants. DERGA algorithm, which employs multiple classification algorithms, determined the optimal pattern of these key variants, resulting in 97% accuracy for predicting disease outcome. Individual variations ranged from 40 to 161 variants per patient, with 977 total variants detected. This study demonstrates the utility of alpha-index in ranking a substantial number of genetic variants. This approach enables the implementation of well-established classification algorithms that effectively determine the relevance of genetic variants in predicting outcomes with high accuracy.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
J Cell Mol Med
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2024
Tipo del documento:
Article
País de afiliación:
Grecia