Prediction of Compound Profiling Matrices Using Machine Learning.

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Prediction of Compound Profiling Matrices Using Machine Learning.

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2018 nî lūn-bûn

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2018年の論文

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年學術文章

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2018年學術文章

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2018年學術文章

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Prediction of Compound Profiling Matrices Using Machine Learning.

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Prediction of Compound Profiling Matrices Using Machine Learning.

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Prediction of Compound Profiling Matrices Using Machine Learning.

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Prediction of Compound Profiling Matrices Using Machine Learning.

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Prediction of Compound Profiling Matrices Using Machine Learning.

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Prediction of Compound Profiling Matrices Using Machine Learning.

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P1476

Prediction of Compound Profiling Matrices Using Machine Learning

@en

P2093

Martin Vogt

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Swarit Jasial

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Tomoyuki Miyao

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P2860

The ChEMBL bioactivity database: an update

Comprehensive characterization of the Published Kinase Inhibitor Set

PubChem BioAssay: 2017 update

Extended-connectivity fingerprints

A small molecule-kinase interaction map for clinical kinase inhibitors

High-throughput screening of historic collections: observations on file size, biological targets, and file diversity.

An efficient algorithm coupled with synthetic minority over-sampling technique to classify imbalanced PubChem BioAssay data.

QSAR modeling of imbalanced high-throughput screening data in PubChem.

Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem.

Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data.

P304

4713-4723

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P31

scholarly article

P356

10.1021/ACSOMEGA.8B00462

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P433

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P478

http://www.w3.org/2001/XMLSchema#string

P50

Jürgen Bajorath

Raquel Rodríguez-Pérez

P577

2018-04-01T00:00:00Z

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P698

30023899

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P921

machine learning

P932

6045364

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