scispace - formally typeset
J

Jochen Kruppa

Researcher at Humboldt University of Berlin

Publications -  73
Citations -  3612

Jochen Kruppa is an academic researcher from Humboldt University of Berlin. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 20, co-authored 65 publications receiving 2791 citations. Previous affiliations of Jochen Kruppa include University of Lübeck & University of Göttingen.

Papers
More filters
Journal ArticleDOI

Loss-of-function mutations in APOC3, triglycerides, and coronary disease

Jacy R Crosby, +96 more
TL;DR: Rare mutations that disrupt AP OC3 function were associated with lower levels of plasma triglycerides and APOC3, and carriers of these mutations were found to have a reduced risk of coronary heart disease.
Journal ArticleDOI

Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics

TL;DR: This paper synthesizes 10 years of RF development with emphasis on applications to bioinformatics and computational biology and some representative examples of RF applications in this context and possible directions for future research.
Journal ArticleDOI

Coding Variation in ANGPTL4, LPL, and SVEP1 and the Risk of Coronary Disease

Nathan O. Stitziel, +128 more
TL;DR: It was found that carriers of loss-of-function mutations in ANGPTL4 had triglyceride levels that were lower than those among noncarriers; these mutations were also associated with protection from coronary artery disease.
Journal ArticleDOI

Systematic Evaluation of Pleiotropy Identifies 6 Further Loci Associated With Coronary Artery Disease

Tom R. Webb, +138 more
TL;DR: Several CAD loci show substantial pleiotropy, which may help us understand the mechanisms by which these loci affect CAD risk, and identify 6 new loci associated with CAD at genome-wide significance.
Journal ArticleDOI

Probability machines: consistent probability estimation using nonparametric learning machines.

TL;DR: Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses.