{Reference Type}: Journal Article
{Title}: Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm.
{Author}: Asteris PG;Gandomi AH;Armaghani DJ;Tsoukalas MZ;Gavriilaki E;Gerber G;Konstantakatos G;Skentou AD;Triantafyllidis L;Kotsiou N;Braunstein E;Chen H;Brodsky R;Touloumenidou T;Sakellari I;Alkayem NF;Bardhan A;Cao M;Cavaleri L;Formisano A;Guney D;Hasanipanah M;Khandelwal M;Mohammed AS;Samui P;Zhou J;Terpos E;Dimopoulos MA;
{Journal}: J Cell Mol Med
{Volume}: 28
{Issue}: 4
{Year}: 2024 02
{Factor}: 5.295
{DOI}: 10.1111/jcmm.18105
{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.