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Updated: Jul 3, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

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

Panagiotis G Asteris1, Amir H Gandomi2,3, Danial J Armaghani4

  • 1Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Athens, Greece.

Journal of Cellular and Molecular Medicine
|February 10, 2024
PubMed

Insights

Predicting COVID-19 severity is crucial. This study identified key complement genetic variants using AI, achieving 97% accuracy in predicting patient outcomes and hospital admission needs.

Area of Science:

  • Immunology
  • Genetics
  • Computational Biology

Background:

  • Complement inhibition shows therapeutic potential in diseases like COVID-19.
  • Accurate prediction of disease severity is essential for patient management.
  • Identifying genetic factors influencing complement activity is critical for personalized medicine.

Purpose of the Study:

  • To identify key complement genetic variants associated with COVID-19 outcomes.
  • To develop an AI-based prediction tool for disease severity.
  • To determine an optimal pattern of genetic variants for accurate outcome prediction.

Main Methods:

  • Analysis of genetic data from 204 hospitalized COVID-19 patients.
  • Utilized an artificial intelligence (AI)-based algorithm for outcome prediction (ICU vs. non-ICU admission).
  • Employed the alpha-index to identify predictive genetic variants and the DERGA algorithm for pattern determination.

Main Results:

  • Identified 30 highly predictive complement genetic variants using the alpha-index.
  • The DERGA algorithm achieved 97% accuracy in predicting COVID-19 disease outcome.
  • Detected a total of 977 variants, with individual variations ranging from 40 to 161 per patient.

Conclusions:

  • The alpha-index is effective for ranking numerous genetic variants.
  • AI-driven analysis of complement genetic variants enables highly accurate prediction of disease outcomes.
  • This approach facilitates the development of predictive tools for personalized COVID-19 management.

Keywords:
COVID-19DERGASARS-CoV2artificial intelligenceclassification algorithmsgeneticvariants

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