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

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.

PMID: 38339761
PMCID: PMC10863978
DOI: 10.1111/jcmm.18105

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

Panagiotis G Asteris et al. J Cell Mol Med. 2024 Feb.

. 2024 Feb;28(4):e18105.

doi: 10.1111/jcmm.18105.

Authors

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.

PMID: 38339761
PMCID: PMC10863978
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.

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

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Conflict of interest statement

Gloria Gerber received honoraria from Apellis Pharmaceutical. Gloria Gerber employment (spouse) and stock holder (spouse) at Pfizer. Eleni Gavriilaki is supported by the ASH Global Research Award and has consulted for Omeros Cooperation. The rest of authors do not have any conflicts of interest to disclose.

Figures

**FIGURE 1**
Study population categorized by age, gender and disease severity (requiring or not hospitalization in intensive care unit [ICU]).

**FIGURE 2**
Ranking of the top 30 genetic variants based on the proposed new alpha‐index. Red colour signifies that the occurrence of the variant is dominating in patients admitted to ICU, while blue signifies the occurrence of the variant for those not admitted in ICU.

**FIGURE 3**
Accuracy plots for the DERGA procedure for each used classification algorithm.

See this image and copyright information in PMC

References

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

Affiliations

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

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

LinkOut - more resources

Full Text Sources

Medical

Miscellaneous