مورد إلكتروني
A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study.
| العنوان: | A generalized covariate-adjusted top-scoring pair algorithm with applications to diabetic kidney disease stage classification in the Chronic Renal Insufficiency Cohort (CRIC) Study. |
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| المؤلفون: | Kwan, Brian |
| المصدر: | BMC bioinformatics; vol 24, iss 1, 57; 1471-2105 |
| بيانات النشر: | eScholarship, University of California 2023-02-01 |
| تفاصيل مُضافة: | Kwan, Brian Fuhrer, Tobias Montemayor, Daniel Fink, Jeffery C He, Jiang Hsu, Chi-Yuan Messer, Karen Nelson, Robert G Pu, Minya Ricardo, Ana C Rincon-Choles, Hernan Shah, Vallabh O Ye, Hongping Zhang, Jing Sharma, Kumar Natarajan, Loki |
| نوع الوثيقة: | Electronic Resource |
| مستخلص: | BackgroundThe growing amount of high dimensional biomolecular data has spawned new statistical and computational models for risk prediction and disease classification. Yet, many of these methods do not yield biologically interpretable models, despite offering high classification accuracy. An exception, the top-scoring pair (TSP) algorithm derives parameter-free, biologically interpretable single pair decision rules that are accurate and robust in disease classification. However, standard TSP methods do not accommodate covariates that could heavily influence feature selection for the top-scoring pair. Herein, we propose a covariate-adjusted TSP method, which uses residuals from a regression of features on the covariates for identifying top scoring pairs. We conduct simulations and a data application to investigate our method, and compare it to existing classifiers, LASSO and random forests.ResultsOur simulations found that features that were highly correlated with clinical variables had high likelihood of being selected as top scoring pairs in the standard TSP setting. However, through residualization, our covariate-adjusted TSP was able to identify new top scoring pairs, that were largely uncorrelated with clinical variables. In the data application, using patients with diabetes (n = 977) selected for metabolomic profiling in the Chronic Renal Insufficiency Cohort (CRIC) study, the standard TSP algorithm identified (valine-betaine, dimethyl-arg) as the top-scoring metabolite pair for classifying diabetic kidney disease (DKD) severity, whereas the covariate-adjusted TSP method identified the pair (pipazethate, octaethylene glycol) as top-scoring. Valine-betaine and dimethyl-arg had, respectively, ≥ 0.4 absolute correlation with urine albumin and serum creatinine, known prognosticators of DKD. Thus without covariate-adjustment the top-scoring pair largely reflected known markers of disease severity, whereas covariate-adjusted TSP uncovered features liberated from conf |
| مصطلحات الفهرس: | Humans, Diabetic Nephropathies, Diabetes Mellitus, Betaine, Algorithms, Renal Insufficiency, Chronic, Metabolomics, Biomarker, Classification, Feature selection, Kidney disease, Order statistics, Ranking algorithm, Diabetes, Kidney Disease, Clinical Research, Renal and urogenital, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics, article |
| URL: | |
| الإتاحة: | Open access content. Open access content public |
| ملاحظة: | application/pdf BMC bioinformatics vol 24, iss 1, 57 1471-2105 |
| أرقام أخرى: | CDLER oai:escholarship.org:ark:/13030/qt004995rm qt004995rm https://escholarship.org/uc/item/004995rmTest https://escholarship.orgTest/ 1393990161 |
| المصدر المساهم: | UC MASS DIGITIZATION From OAIster®, provided by the OCLC Cooperative. |
| رقم الانضمام: | edsoai.on1393990161 |
| قاعدة البيانات: | OAIster |
| الوصف غير متاح. |