Ecosyste.ms: Papers

An open API service providing mapping between scientific papers and software projects that are mentioned in them.
All mentions data is based on the CZI Software Mentions dataset.

Projects: cran: hyperSMURF

https://packages.ecosyste.ms/registries/cran.r-project.org/packages/hyperSMURF

Hyper-Ensemble Smote Undersampled Random Forests
5 versions
Latest release: over 6 years ago
0 downloads

Papers Mentioning hyperSMURF 6

10.1186/s12859-019-2877-3

GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data
Cited by: 19
Author(s): Enrico Mossotto, James J. Ashton, Luke O’Gorman, Reuben J. Pengelly, R Mark Beattie, Ben D. MacArthur, Sarah Ennis
Software Mentions: 2
Published: over 5 years ago

10.3389/fgene.2020.00350

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci
Cited by: 91
Author(s): Hannah Nicholls, Christopher R. John, David S. Watson, Patricia B. Munroe, Michael R. Barnes, Claudia Cabrera
Software Mentions: 2
Published: over 4 years ago

10.1186/s13059-018-1546-6

PINES: phenotype-informed tissue weighting improves prediction of pathogenic noncoding variants
Cited by: 17
Author(s): Corneliu Bodea, Adele A. Mitchell, Alex Bloemendal, Aaron G. Day‐Williams, Heiko Runz, Shamil Sunyaev
Software Mentions: 2
Published: almost 6 years ago

10.3389/fpubh.2020.00178

Over- and Under-sampling Approach for Extremely Imbalanced and Small Minority Data Problem in Health Record Analysis
Cited by: 30
Author(s): Koichi Fujiwara, Yukun Huang, Kazuko Hori, Kenichi Nishioji, Masao Kobayashi, Mai Kamaguchi, Manabu Kano
Software Mentions: 1
Published: over 4 years ago

10.1093/gigascience/giaa052

parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants
Cited by: 8
Author(s): Alessandro Petrini, Marco Mesiti, Max Schubach, Daniel Daniš, Matteo Ré, Giuliano Grossi, Luca Cappelletti, Tiziana Castrignanò, Peter N. Robinson, Giorgio Valentini
Software Mentions: 1
Published: over 4 years ago

10.1038/s41598-017-03011-5

Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants
Cited by: 61
Author(s): Max Schubach, Matteo Ré, Peter N. Robinson, Giorgio Valentini
Software Mentions: 1
Published: over 7 years ago