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Article: 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

TitleUsing 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
Authors
KeywordsRandom forest
Support vector machine
Diabetic kidney disease
Machine learning
Genotypes
Phenotypes
Prediction
Issue Date2013
Citation
BMC Nephrology, 2013, v. 14, n. 1 How to Cite?
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). Methods. In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset. Results: Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models. Conclusions: Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD. © 2013 Leung et al.; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/222143

 

DC FieldValueLanguage
dc.contributor.authorLeung, Ross K K-
dc.contributor.authorWang, Ying-
dc.contributor.authorMa, Ronald C W-
dc.contributor.authorLuk, Andrea O Y-
dc.contributor.authorLam, Vincent-
dc.contributor.authorNg, Maggie-
dc.contributor.authorSo, Wing Yee-
dc.contributor.authorTsui, Stephen K W-
dc.contributor.authorChan, Juliana C N-
dc.date.accessioned2015-12-21T06:48:52Z-
dc.date.available2015-12-21T06:48:52Z-
dc.date.issued2013-
dc.identifier.citationBMC Nephrology, 2013, v. 14, n. 1-
dc.identifier.urihttp://hdl.handle.net/10722/222143-
dc.description.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). Methods. In a prospective cohort of type 2 diabetic patients, we selected 119 subjects with DKD and 554 without DKD at enrolment and after a median follow-up period of 7.8 years for model training, testing and validation using seven machine learning methods (partial least square regression, the classification and regression tree, the C5.0 decision tree, random forest, naïve Bayes classification, neural network and support vector machine). We used 17 clinical attributes and 70 single nucleotide polymorphisms (SNPs) of 54 candidate genes to build different models. The top attributes selected by the best-performing models were then used to build models with performance comparable to those using the entire dataset. Results: Age, age of diagnosis, systolic blood pressure and genetic polymorphisms of uteroglobin and lipid metabolism were selected by most methods. Models generated by support vector machine (svmRadial) and random forest (cforest) had the best prediction accuracy whereas models derived from naïve Bayes classifier and partial least squares regression had the least optimal performance. Using 10 clinical attributes (systolic and diastolic blood pressure, age, age of diagnosis, triglyceride, white blood cell count, total cholesterol, waist to hip ratio, LDL cholesterol, and alcohol intake) and 5 genetic attributes (UGB G38A, LIPC -514C > T, APOB Thr71Ile, APOC3 3206T > G and APOC3 1100C > T), selected most often by SVM and cforest, we were able to build high-performance models. Conclusions: Amongst different machine learning methods, svmRadial and cforest had the best performance. Genetic polymorphisms related to inflammation and lipid metabolism warrant further investigation for their associations with DKD. © 2013 Leung et al.; licensee BioMed Central Ltd.-
dc.languageeng-
dc.relation.ispartofBMC Nephrology-
dc.subjectRandom forest-
dc.subjectSupport vector machine-
dc.subjectDiabetic kidney disease-
dc.subjectMachine learning-
dc.subjectGenotypes-
dc.subjectPhenotypes-
dc.subjectPrediction-
dc.titleUsing 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-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1186/1471-2369-14-162-
dc.identifier.pmid23879411-
dc.identifier.scopuseid_2-s2.0-84880335873-
dc.identifier.volume14-
dc.identifier.issue1-
dc.identifier.eissn1471-2369-

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