Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis
Source of Publication
Informatics in Medicine Unlocked
Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningococcal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition in gene function pathways from nuclear to cytoplasmic to extracellular, corresponding with Pediatric Logistic Organ Dysfunction score (PELOD) readings at 0, 24, and 48 h. ANN was the most accurate of the six ML models applied for survival prediction. This study successfully correlated PELOD with transcriptomic data, mapping enriched GE modules in acute sepsis. By integrating network analysis methods to identify key gene modules and using machine learning for sepsis prognosis, this study offers valuable insights for precision-based treatment strategies in future research. The observed temporal-spatial pattern of cellular recovery in sepsis could prove useful in guiding clinical management and therapeutic interventions.
Artificial neural network, Gene modular approach, Machine learning, Meningococcal septic shock
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Rashid, Asrar; Anwary, Arif R.; Al-Obeidat, Feras; Brierley, Joe; Uddin, Mohammed; Alkhzaimi, Hoda; Sarpal, Amrita; Toufiq, Mohammed; Malik, Zainab A.; Kadwa, Raziya; Khilnani, Praveen; Shaikh, M. Guftar; Benakatti, Govind; Sharief, Javed; Zaki, Syed Ahmed; Zeyada, Abdulrahman; Al-Dubai, Ahmed; Hafez, Wael; and Hussain, Amir, "Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis" (2023). All Works. 5935.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series