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

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

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

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

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: J Cell Mol Med, 2024, 28, (4), pp. e18105
Issue Date:: 2024-02

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Field	Value	Language
dc.contributor.author	Asteris, PG
dc.contributor.author	Gandomi, AH
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Tsoukalas, MZ
dc.contributor.author	Gavriilaki, E
dc.contributor.author	Gerber, G
dc.contributor.author	Konstantakatos, G
dc.contributor.author	Skentou, AD
dc.contributor.author	Triantafyllidis, L
dc.contributor.author	Kotsiou, N
dc.contributor.author	Braunstein, E
dc.contributor.author	Chen, H
dc.contributor.author	Brodsky, R
dc.contributor.author	Touloumenidou, T
dc.contributor.author	Sakellari, I
dc.contributor.author	Alkayem, NF
dc.contributor.author	Bardhan, A
dc.contributor.author	Cao, M
dc.contributor.author	Cavaleri, L
dc.contributor.author	Formisano, A
dc.contributor.author	Guney, D
dc.contributor.author	Hasanipanah, M
dc.contributor.author	Khandelwal, M
dc.contributor.author	Mohammed, AS
dc.contributor.author	Samui, P
dc.contributor.author	Zhou, J
dc.contributor.author	Terpos, E
dc.contributor.author	Dimopoulos, MA
dc.date.accessioned	2024-08-06T01:44:53Z
dc.date.available	2023-11-22
dc.date.available	2024-08-06T01:44:53Z
dc.date.issued	2024-02
dc.identifier.citation	J Cell Mol Med, 2024, 28, (4), pp. e18105
dc.identifier.issn	1582-1838
dc.identifier.issn	1582-4934
dc.identifier.uri	http://hdl.handle.net/10453/180056
dc.description.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.
dc.format	Print
dc.language	eng
dc.publisher	WILEY
dc.relation.ispartof	J Cell Mol Med
dc.relation.isbasedon	10.1111/jcmm.18105
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0304 Medicinal and Biomolecular Chemistry, 0601 Biochemistry and Cell Biology, 1103 Clinical Sciences
dc.subject.classification	Biochemistry & Molecular Biology
dc.subject.classification	3101 Biochemistry and cell biology
dc.subject.classification	3404 Medicinal and biomolecular chemistry
dc.subject.mesh	Humans
dc.subject.mesh	COVID-19
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Algorithms
dc.subject.mesh	Humans
dc.subject.mesh	Algorithms
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	COVID-19
dc.subject.mesh	Humans
dc.subject.mesh	COVID-19
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Algorithms
dc.title	Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm.
dc.type	Journal Article
utslib.citation.volume	28
utslib.location.activity	England
utslib.for	0304 Medicinal and Biomolecular Chemistry
utslib.for	0601 Biochemistry and Cell Biology
utslib.for	1103 Clinical Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-08-06T01:44:48Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	28
utslib.citation.issue	4

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.

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http://hdl.handle.net/10453/180056