Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis-Reference-Cited by-同舟云学术

Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsis

Published:2023 Issue: Volume:41 Page:101293
ISSN:2352-9148
Container-title:Informatics in Medicine Unlocked
language:en
Short-container-title:Informatics in Medicine Unlocked

Author:

Rashid Asrar^ORCID,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,Hussain Amir

Publisher

Elsevier BV

Subject

Health Informatics

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4. Human toll-like receptor 4 mutations are associated with susceptibility to invasive meningococcal disease in infancy;Faber;Pediatr Infect Dis J,2006

5. Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum;Wong;Crit Care Med,2009

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