Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm

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.PMID:38339761 | PMC:PMC10863978 | DOI:10.1111/jcmm.18105

Source: J Cell Mol Med - February 10, 2024 Category: Molecular Biology Authors: Panagiotis G Asteris Amir H Gandomi Danial J Armaghani Markos Z Tsoukalas Eleni Gavriilaki Gloria Gerber Gerasimos Konstantakatos Athanasia D Skentou Leonidas Triantafyllidis Nikolaos Kotsiou Evan Braunstein Hang Chen Robert Brodsky Tasoula Touloumenidou Source Type: research