Coping with unbalanced class data sets in oral absorption models.
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QSAR modeling of imbalanced high-throughput screening data in PubChem.Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.Machine learning for predicting lifespan-extending chemical compoundsIn silico Prediction of Drug-induced Liver Injury Based on Adverse Drug Reaction Reports.Chemical and in vitro biological information to predict mouse liver toxicity using recursive random forests.Estimation of biliary excretion of foreign compounds using properties of molecular structure.Simultaneous Prediction of four ATP-binding Cassette Transporters' Substrates Using Multi-label QSAR.Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources.Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine
P2860
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P2860
Coping with unbalanced class data sets in oral absorption models.
description
2013 nî lūn-bûn
@nan
2013 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2013年の論文
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2013年学术文章
@wuu
2013年学术文章
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2013年学术文章
@zh-hans
2013年学术文章
@zh-my
2013年学术文章
@zh-sg
2013年學術文章
@yue
name
Coping with unbalanced class data sets in oral absorption models.
@ast
Coping with unbalanced class data sets in oral absorption models.
@en
type
label
Coping with unbalanced class data sets in oral absorption models.
@ast
Coping with unbalanced class data sets in oral absorption models.
@en
prefLabel
Coping with unbalanced class data sets in oral absorption models.
@ast
Coping with unbalanced class data sets in oral absorption models.
@en
P356
P1476
Coping with unbalanced class data sets in oral absorption models.
@en
P2093
Danielle Newby
P304
P356
10.1021/CI300348U
P577
2013-01-24T00:00:00Z