Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
about
Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.Toward predictive models for drug-induced liver injury in humans: are we there yet?The Promise of New Technologies to Reduce, Refine, or Replace Animal Use while Reducing Risks of Drug Induced Liver Injury in Pharmaceutical Development.In Silico Models for Hepatotoxicity.Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening.Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information.Determination of the Potential of Drug Candidates to Cause Severe Skin Disorders Using Computational Modeling.QSAR and Predictors of Eye and Skin Effects.PaDEL-DDPredictor: open-source software for PD-PK-T prediction.vNN Web Server for ADMET Predictions.Predicting drug-induced liver injury in human with Naïve Bayes classifier approach.Conformation-dependent QSAR approach for the prediction of inhibitory activity of bromodomain modulators.Prediction Is a Balancing Act: Importance of Sampling Methods to Balance Sensitivity and Specificity of Predictive Models Based on Imbalanced Chemical Data Sets
P2860
Q30681280-3E0C0278-B01A-4204-A4DD-E484746144ABQ33704765-C03F9183-8AAD-4C38-A93A-16AE47EA1D3BQ38187450-06C29549-4E80-4B00-B4AB-6F3D32E303D3Q38774776-752AAD7E-7848-4864-9F52-F4C270AC0D19Q38867269-D664A6E3-65E5-4208-A853-04ECCF8C7F30Q39300204-72BD0B17-251E-4146-814F-8D98E2B18E1CQ39425631-B29350FE-4748-4C38-84B9-DFB78DDE2BE4Q39535536-E4AC49CF-0AE0-432A-9E55-0BA0A7267F99Q39535548-87AF2E85-2CBB-40AD-AF2F-7D435FB92CDEQ42674907-9114B63B-A1E5-4770-90B7-923994445DC6Q47117686-CB22066A-59DD-47D8-8C95-BCE0E9B61D45Q47600233-C6FBF3E7-B423-4CF6-8BAD-EAE01E33A42EQ47984514-878BF011-AF92-4197-8E5D-2793E7478F98Q57131724-A180FE85-1AAA-4B21-93B2-902EC2C55652
P2860
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年学术文章
@wuu
2011年学术文章
@zh
2011年学术文章
@zh-cn
2011年学术文章
@zh-hans
2011年学术文章
@zh-my
2011年学术文章
@zh-sg
2011年學術文章
@yue
2011年學術文章
@zh-hant
name
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
@en
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
@nl
type
label
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
@en
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
@nl
prefLabel
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
@en
Mixed learning algorithms and features ensemble in hepatotoxicity prediction.
@nl
P2860
P1476
Mixed learning algorithms and features ensemble in hepatotoxicity prediction
@en
P2093
Chin Yee Liew
Yen Ching Lim
P2860
P2888
P304
P356
10.1007/S10822-011-9468-3
P50
P577
2011-09-06T00:00:00Z
P6179
1016356420