Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case-control cohort analysis
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AbstractBackground: Multi-causality and heterogeneity of phenotypes and genotypes characterize complex diseases. In a database with comprehensive collection of phenotypes and genotypes, we compared the performance of common machine learning methods to generate mathematical models to predict diabetic kidney disease (DKD).
All Author(s) ListLeung RKK, Wang Y, Ma RCW, Luk AOY, Lam V, Ng M, So WY, Tsui SKW, Chan JCN
Journal nameBMC Nephrology
Detailed description2013 Jul 23;14:162. doi: 10.1186/1471-2369-14-162..
Year2013
Month7
Day23
Volume Number14
PublisherBioMed Central
ISSN1471-2369
LanguagesEnglish-United Kingdom
KeywordsDiabetic kidney disease; Genotypes; Machine learning; Phenotypes; Prediction; Random forest; Support vector machine
Web of Science Subject CategoriesUrology & Nephrology; UROLOGY & NEPHROLOGY

Last updated on 2020-01-07 at 01:58