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

Asteris PG 1 , Gandomi AH 2 , Armaghani DJ 3 , Tsoukalas MZ 1 , Gavriilaki E 4 , Gerber G 5 Show all authors , Konstantakatos G 1 , Skentou AD 1 , Triantafyllidis L 1 , Kotsiou N 4 , Braunstein E 5 , Chen H 5 , Brodsky R 5 , Touloumenidou T 6 , Sakellari I 6 , Alkayem NF 7 , Bardhan A 8 , Cao M 9 , Cavaleri L 10 , Formisano A 11 , Guney D 12 , Hasanipanah M 13 , Khandelwal M 14 , Mohammed AS 15 , Samui P 8 , Zhou J 16 , Terpos E 17 , Dimopoulos MA 17

Affiliations 

  • 1 Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece
  • 2 Faculty of Engineering & IT, University of Technology Sydney, Sydney, New South Wales, Australia
  • 3 School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, Australia
  • 4 2nd Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
  • 5 Hematology Division, Johns Hopkins University, Baltimore, USA
  • 6 Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece
  • 7 College of Civil and Transportation Engineering, Hohai University, Nanjing, China
  • 8 Civil Engineering Department, National Institute of Technology Patna, Patna, India
  • 9 Department of Engineering Mechanics, Hohai University, Nanjing, China
  • 10 Department of Civil, Environmental, Aerospace and Materials Engineering, University of Palermo, Palermo, Italy
  • 11 Department of Structures for Engineering and Architecture, University of Naples "Federico II", Naples, Italy
  • 12 Engineering Faculty, San Diego State University, San Diego, California, USA
  • 13 Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
  • 14 Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, Victoria, Australia
  • 15 Engineering Department, American University of Iraq, Sulaymaniyah, Iraq
  • 16 School of Resources and Safety Engineering, Central South University, Changsha, China
  • 17 Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece
J Cell Mol Med, 2024 Feb;28(4):e18105.
PMID: 38339761 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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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