Genetic justification of COVID-19 patient outcomes using DERGA, a novel data ensemble refinement greedy algorithm.
Panagiotis G AsterisAmir H GandomiDanial J ArmaghaniMarkos Z TsoukalasEugenia GkaliagkousiGloria GerberGerasimos KonstantakatosAthanasia D SkentouLeonidas TriantafyllidisNikolaos KotsiouEvan BraunsteinHang ChenRobert BrodskyTasoula TouloumenidouIoanna SakellariNizar Faisal AlkayemAbidhan BardhanMaosen CaoLiborio CavaleriAntonio FormisanoDeniz GuneyMahdi HasanipanahManoj KhandelwalAhmed Salih MohammedPijush SamuiJian ZhouEvangelos TerposMeletios A DimopoulosPublished in: Journal of cellular and molecular medicine (2024)
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.
Keyphrases
- machine learning
- deep learning
- artificial intelligence
- big data
- copy number
- sars cov
- coronavirus disease
- convolutional neural network
- intensive care unit
- primary care
- healthcare
- genome wide
- emergency department
- electronic health record
- type diabetes
- gene expression
- neural network
- skeletal muscle
- dna methylation
- high resolution
- mass spectrometry
- acute respiratory distress syndrome
- data analysis