Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis
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...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914823001399 |
| _version_ | 1797690916252680192 |
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| author | Asrar Rashid Arif R. Anwary Feras Al-Obeidat Joe Brierley Mohammed Uddin Hoda Alkhzaimi Amrita Sarpal Mohammed Toufiq Zainab A. Malik Raziya Kadwa Praveen Khilnani M Guftar Shaikh Govind Benakatti Javed Sharief Syed Ahmed Zaki Abdulrahman Zeyada Ahmed Al-Dubai Wael Hafez Amir Hussain |
| author_facet | Asrar Rashid Arif R. Anwary Feras Al-Obeidat Joe Brierley Mohammed Uddin Hoda Alkhzaimi Amrita Sarpal Mohammed Toufiq Zainab A. Malik Raziya Kadwa Praveen Khilnani M Guftar Shaikh Govind Benakatti Javed Sharief Syed Ahmed Zaki Abdulrahman Zeyada Ahmed Al-Dubai Wael Hafez Amir Hussain |
| author_sort | Asrar Rashid |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T02:05:58Z |
| format | Article |
| id | doaj.art-4337dceefb524a2694d6121bf865be05 |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-03-12T02:05:58Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-4337dceefb524a2694d6121bf865be052023-09-07T04:44:10ZengElsevierInformatics in Medicine Unlocked2352-91482023-01-0141101293Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsisAsrar Rashid0Arif R. Anwary1Feras Al-Obeidat2Joe Brierley3Mohammed Uddin4Hoda Alkhzaimi5Amrita Sarpal6Mohammed Toufiq7Zainab A. Malik8Raziya Kadwa9Praveen Khilnani10M Guftar Shaikh11Govind Benakatti12Javed Sharief13Syed Ahmed Zaki14Abdulrahman Zeyada15Ahmed Al-Dubai16Wael Hafez17Amir Hussain18Edinburgh Napier University, Merchiston Campus, 10 Colinton Road, Edinburgh, Scotland, EH10 5DT, UK; NMC Royal Khalifa Hospital, Abu Dhabi, United Arab Emirates; Corresponding author. Dr Asrar Rashid, Edinburgh Napier University, Department of Computer Science, Merchiston Campus, 10 Colinton Road, Edinburgh, Scotland, EH10 5DT, UK.Edinburgh Napier University, Merchiston Campus, 10 Colinton Road, Edinburgh, Scotland, EH10 5DT, UKCollege of Technological Innovation at Zayed University, Abu Dhabi, United Arab EmiratesGreat Ormond Street Children's Hospital, London, UKCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab EmiratesNew York University Abu Dhabi, United Arab EmiratesWeill Cornell Medicine, Doha, Qatar; Sidra Medicine, Doha, QatarThe Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USACollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates; Mediclinic City Hospital, Dubai, United Arab EmiratesNMC Royal Khalifa Hospital, Abu Dhabi, United Arab EmiratesMedanta Gururam, Delhi, IndiaRoyal Hospital for Children, Glasgow, UKYas Clinic, Abu Dhabi, United Arab EmiratesNMC Royal Khalifa Hospital, Abu Dhabi, United Arab EmiratesAll India Institute of Medical Sciences, Bibinagar, Hyderabad, IndiaNMC Royal Khalifa Hospital, Abu Dhabi, United Arab EmiratesEdinburgh Napier University, Merchiston Campus, 10 Colinton Road, Edinburgh, Scotland, EH10 5DT, UKNMC Royal Khalifa Hospital, Abu Dhabi, United Arab Emirates; Medical Research Division, Department of Internal Medicine, The National Research Centre, Cairo, EgyptEdinburgh Napier University, Merchiston Campus, 10 Colinton Road, Edinburgh, Scotland, EH10 5DT, UKSepsis 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.http://www.sciencedirect.com/science/article/pii/S2352914823001399Meningococcal septic shockMachine learningArtificial neural networkGene modular approach |
| spellingShingle | Asrar Rashid Arif R. Anwary Feras Al-Obeidat Joe Brierley Mohammed Uddin Hoda Alkhzaimi Amrita Sarpal Mohammed Toufiq Zainab A. Malik Raziya Kadwa Praveen Khilnani M Guftar Shaikh Govind Benakatti Javed Sharief Syed Ahmed Zaki Abdulrahman Zeyada Ahmed Al-Dubai Wael Hafez Amir Hussain Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis Informatics in Medicine Unlocked Meningococcal septic shock Machine learning Artificial neural network Gene modular approach |
| title | Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis |
| title_full | Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis |
| title_fullStr | Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis |
| title_full_unstemmed | Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis |
| title_short | Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis |
| title_sort | application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis |
| topic | Meningococcal septic shock Machine learning Artificial neural network Gene modular approach |
| url | http://www.sciencedirect.com/science/article/pii/S2352914823001399 |
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