2016/07/29 by Stephen Rush, Stephen T Rush, Christine H. Lee +8
Biochemistry, Genetics and Molecular Biology · Mathematics · Medicine · #62P10 #Clostridium difficile and Clostridium perfringens research #FOS: Biological sciences #FOS: Computer and information sciences #Gut microbiota and health #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM) #Urinary Tract Infections Management #msc:62P10 #q-bio.QM #stat.ML
paper · pdf · doi:10.48550/arxiv.1607.08877
31 pages, 6 figures, 5 tables
arxiv created 2016/07/29 · openalex publication_date 2016/07/29 · arxiv updated 2016/08/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Scientific investigations that incorporate next generation sequencing involve analyses of high-dimensional data where the need to organize, collate and interpret the outcomes are pressingly important. Currently, data can be collected at the microbiome level leading to the possibility of personalized medicine whereby treatments can be tailored at this scale. In this paper, we lay down a statistical framework for this type of analysis with a view toward synthesis of products tailored to individual patients. Although the paper applies the technique to data for a particular infectious disease, the methodology is sufficiently rich to be expanded to other problems in medicine, especially those in which coincident `-omics' covariates and clinical responses are simultaneously captured.