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Benjamin J. Keller

Researcher at Eastern Michigan University

Publications -  30
Citations -  1643

Benjamin J. Keller is an academic researcher from Eastern Michigan University. The author has contributed to research in topics: Recommender system & Candidate gene. The author has an hindex of 15, co-authored 30 publications receiving 1436 citations. Previous affiliations of Benjamin J. Keller include University of Washington & University of Michigan.

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Proceedings ArticleDOI

Evaluating and improving fault localization

TL;DR: A design space is identified that includes many previously-studied fault localization techniques as well as hundreds of new techniques, and which factors in the design space are most important, using an overall set of 395 real faults.
Journal ArticleDOI

New susceptibility loci associated with kidney disease in type 1 diabetes.

Niina Sandholm, +105 more
- 20 Sep 2012 - 
TL;DR: A meta-analysis of genome-wide association studies of T1D DN revealed association of two SNPs with ESRD in the AFF3 gene and an intergenic SNP on chromosome 15q26 between the genes RGMA and MCTP2, which represent new signals in the pathogenesis of DN.
Journal ArticleDOI

Privacy risks in recommender systems

TL;DR: Recommender system users who rate items across disjoint domains face a privacy risk analogous to the one that occurs with statistical database queries.
Journal ArticleDOI

Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Sudha K. Iyengar, +59 more
- 25 Aug 2015 - 
TL;DR: A novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups is identified with directionally consistent results across ethnic groups and provides insight into the genetic architecture of DKD.
Journal ArticleDOI

Studying Recommendation Algorithms by Graph Analysis

TL;DR: This approach emphasizes reachability via an algorithm within the implicit graph structure underlying a recommender dataset and allows us to consider questions relating algorithmic parameters to properties of the datasets.