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.

Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm / Asteris, P. G.; Gandomi, A. H.; Armaghani, D. J.; Tsoukalas, M. Z.; Gavriilaki, E.; Gerber, G.; Konstantakatos, G.; Skentou, A. D.; Triantafyllidis, L.; Kotsiou, N.; Braunstein, E.; Chen, H.; Brodsky, R.; Touloumenidou, T.; Sakellari, I.; Alkayem, N. F.; Bardhan, A.; Cao, M.; Cavaleri, L.; Formisano, A.; Guney, D.; Hasanipanah, M.; Khandelwal, M.; Mohammed, A. S.; Samui, P.; Zhou, J.; Terpos, E.; Dimopoulos, M. A.. - In: JOURNAL OF CELLULAR AND MOLECULAR MEDICINE. - ISSN 1582-1838. - 28:4(2024), pp. 1-10. [10.1111/jcmm.18105]

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

Formisano A.;
2024

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.
2024
Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm / Asteris, P. G.; Gandomi, A. H.; Armaghani, D. J.; Tsoukalas, M. Z.; Gavriilaki, E.; Gerber, G.; Konstantakatos, G.; Skentou, A. D.; Triantafyllidis, L.; Kotsiou, N.; Braunstein, E.; Chen, H.; Brodsky, R.; Touloumenidou, T.; Sakellari, I.; Alkayem, N. F.; Bardhan, A.; Cao, M.; Cavaleri, L.; Formisano, A.; Guney, D.; Hasanipanah, M.; Khandelwal, M.; Mohammed, A. S.; Samui, P.; Zhou, J.; Terpos, E.; Dimopoulos, M. A.. - In: JOURNAL OF CELLULAR AND MOLECULAR MEDICINE. - ISSN 1582-1838. - 28:4(2024), pp. 1-10. [10.1111/jcmm.18105]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/989986
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