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

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; Ioanna Sakellari; Nizar Faisal Alkayem; Abidhan Bardhan; Maosen Cao; Liborio Cavaleri; Antonio Formisano; Deniz Guney; Mahdi Hasanipanah; Manoj Khandelwal; Ahmed Salih Mohammed; Pijush Samui; Jian Zhou; Evangelos Terpos; Meletios A Dimopoulos

doi:10.1111/jcmm.18105

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

J Cell Mol Med. 2024 Feb;28(4):e18105. doi: 10.1111/jcmm.18105.

Authors

Panagiotis G Asteris¹, Amir H Gandomi^{2

3}, Danial J Armaghani⁴, Markos Z Tsoukalas¹, Eleni Gavriilaki⁵, Gloria Gerber⁶, Gerasimos Konstantakatos¹, Athanasia D Skentou¹, Leonidas Triantafyllidis¹, Nikolaos Kotsiou⁵, Evan Braunstein⁶, Hang Chen⁶, Robert Brodsky⁶, Tasoula Touloumenidou⁷, Ioanna Sakellari⁷, Nizar Faisal Alkayem⁸, Abidhan Bardhan⁹, Maosen Cao¹⁰, Liborio Cavaleri¹¹, Antonio Formisano¹², Deniz Guney¹³, Mahdi Hasanipanah¹⁴, Manoj Khandelwal¹⁵, Ahmed Salih Mohammed¹⁶, Pijush Samui⁹, Jian Zhou¹⁷, Evangelos Terpos¹⁸, Meletios A Dimopoulos¹⁸

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 University Research and Innovation Center (EKIK), Óbuda University, Budapest, Hungary.
⁴ School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.
⁵ 2nd Propedeutic Department of Internal Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
⁶ Hematology Division, Johns Hopkins University, Baltimore, USA.
⁷ Hematology Department - BMT Unit, G Papanicolaou Hospital, Thessaloniki, Greece.
⁸ College of Civil and Transportation Engineering, Hohai University, Nanjing, China.
⁹ Civil Engineering Department, National Institute of Technology Patna, Patna, India.
¹⁰ Department of Engineering Mechanics, Hohai University, Nanjing, China.
¹¹ Department of Civil, Environmental, Aerospace and Materials Engineering, University of Palermo, Palermo, Italy.
¹² Department of Structures for Engineering and Architecture, University of Naples "Federico II", Naples, Italy.
¹³ Engineering Faculty, San Diego State University, San Diego, California, USA.
¹⁴ Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
¹⁵ Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, Victoria, Australia.
¹⁶ Engineering Department, American University of Iraq, Sulaymaniyah, Iraq.
¹⁷ School of Resources and Safety Engineering, Central South University, Changsha, China.
¹⁸ Department of Clinical Therapeutics, Medical School, Faculty of Medicine, National Kapodistrian University of Athens, Athens, Greece.

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.

Keywords: COVID-19; DERGA; SARS-CoV2; artificial intelligence; classification algorithms; genetic; variants.

MeSH terms

Algorithms
Artificial Intelligence
COVID-19* / epidemiology
COVID-19* / genetics
Humans