(Q62499440)

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The use of machine learning and nonlinear statistical tools for ADME prediction

scientific article published on 01 February 2009

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19239395

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https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:19239395%20AND%20SRC:MED&resulttype=core&format=json

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22 January 2020

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The use of machine learning and nonlinear statistical tools for ADME prediction (English)

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Europe PubMed Central

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19239395

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https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:19239395%20AND%20SRC:MED&resulttype=core&format=json

22 January 2020

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Yojiro Sakiyama

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19239395

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1 February 2009

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19239395

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22 January 2020

published in

Expert Opinion on Drug Metabolism & Toxicology

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19239395

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5

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2

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19239395

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22 January 2020

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149-169

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19239395

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describes a project that uses

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Random forest: a classification and regression tool for compound classification and QSAR modeling

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COCI

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https://opencitations.net/index/coci/api/v1/citations/10.1021/CI034160G

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11 September 2021

Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM Workshop 52

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https://opencitations.net/index/api/v1/citations/10.1177/026119290503300209

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18 January 2024

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DOI

10.1517/17425250902753261

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Europe PubMed Central

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19239395

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https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:19239395%20AND%20SRC:MED&resulttype=core&format=json

retrieved

22 January 2020

PubMed publication ID

19239395

1 reference

stated in

Europe PubMed Central

PubMed publication ID

19239395

reference URL

https://www.ebi.ac.uk/europepmc/webservices/rest/search?query=EXT_ID:19239395%20AND%20SRC:MED&resulttype=core&format=json

(Q62499440)

The use of machine learning and nonlinear statistical tools for ADME prediction

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