(Q27902316)

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ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

scientific article (publication date: 2016)

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scholarly article

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ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling (English)

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relevance vector machine

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object named as

Tailong Lei

1

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6

object named as

Tingjun Hou

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author name string

Youyong Li

2

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Yunlong Song

3

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Dan Li

4

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Huiyong Sun

5

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language of work or name

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publication date

2016

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Journal of Cheminformatics

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8

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1

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6

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copyright license

Creative Commons Attribution 4.0 International

1 February 2016

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April 2022 Public Data File from Crossref

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ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage

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Toxicological screening

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Recent developments of in silico predictions of oral bioavailability

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Recent developments of in silico predictions of intestinal absorption and oral bioavailability

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29 September 2017

Comparison of in silico tools for evaluating rat oral acute toxicity

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Binary classification of a large collection of environmental chemicals from estrogen receptor assays by quantitative structure-activity relationship and machine learning methods.

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Identifiers

10.1186/S13321-016-0117-7

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DBLP publication ID

journals/jcheminf/LeiLSLSH16

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DBLP Dataset 2021-01-02

28 January 2021

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