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Papers: 10.1038/s41598-020-60747-3

https://doi.org/10.1038/s41598-020-60747-3

High-Throughput Screening to Predict Chemical-Assay Interference

Cited by: 25
Author(s): Alexandre Borrel, Ruili Huang, Srilatha Sakamuru, Menghang Xia, Anton Simeonov, Kamel Mansouri, Keith A. Houck, Richard S. Judson, Nicole Kleinstreuer
Published: over 5 years ago

Software Mentions 10

cran: e1071
Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien
Papers that mentioned: 419
Very Likely Science (85)
cran: kit
Data Manipulation Functions Implemented in C
Papers that mentioned: 42
Very Likely Science (75)
cran: nnet
Feed-Forward Neural Networks and Multinomial Log-Linear Models
Papers that mentioned: 296
Very Likely Science (93)
cran: pls
Partial Least Squares and Principal Component Regression
Papers that mentioned: 137
Very Likely Science (85)
cran: randomForest
Breiman and Cutler's Random Forests for Classification and Regression
Papers that mentioned: 1,447
Very Likely Science (100)
cran: rpart
Recursive Partitioning and Regression Trees
Papers that mentioned: 431
Very Likely Science (100)
pypi: kit
Flask, Celery, SQLAlchemy integration framework.
Papers that mentioned: 42
Very Likely Science (65)
pypi: MolVS
Molecule Validation and Standardization
Papers that mentioned: 25
Very Likely Science (80)
pypi: pls
`pls` is a prettier and powerful `ls` for the pros.
Papers that mentioned: 137
Very Likely Science (65)
pypi: QSAR
Python library for QSAR model from Juvena Machine Learning Team.
Papers that mentioned: 176
Very Likely Science (75)