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An open API service providing mapping between scientific papers and software projects that are mentioned in them.

Papers: 10.1186/s12864-018-4766-y

https://doi.org/10.1186/s12864-018-4766-y

BoostMe accurately predicts DNA methylation values in whole-genome bisulfite sequencing of multiple human tissues

Cited by: 32
Author(s): Luli S. Zou, Michael R. Erdos, D. Leland Taylor, Peter S. Chines, Arushi Varshney, Stephen C. J. Parker, Francis S. Collins, John P. Didion
Published: about 7 years ago

Software Mentions 8

bioconductor: minfi
Analyze Illumina Infinium DNA methylation arrays
Papers that mentioned: 506
Very Likely Science (100)
bioconductor: shinyMethyl
Interactive visualization for Illumina methylation arrays
Papers that mentioned: 18
Very Likely Science (100)
cran: mlr
Machine Learning in R
Papers that mentioned: 64
Very Likely Science (100)
cran: ranger
A Fast Implementation of Random Forests
Papers that mentioned: 91
Very Likely Science (100)
cran: xgboost
Extreme Gradient Boosting
Papers that mentioned: 207
Very Likely Science (100)
pypi: mlr
Linear regression utility with inference tests, residual analysis, outlier visualization, multicollinearity test, and other features
Papers that mentioned: 64
Likely Science (40)
pypi: ranger
A Python package for the manipulation of Range objects
Papers that mentioned: 91
Very Likely Science (100)
pypi: xgboost
XGBoost Python Package
Papers that mentioned: 207
Very Likely Science (90)