Binary Classification of a Large Collection of Environmental Chemicals from Estrogen Receptor Assays by Quantitative Structure–Activity Relationship and Machine Learning Methods-Reference-Cited by-同舟云学术

Binary Classification of a Large Collection of Environmental Chemicals from Estrogen Receptor Assays by Quantitative Structure–Activity Relationship and Machine Learning Methods

Published:2013-12-11 Issue:12 Volume:53 Page:3244-3261
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Zang Qingda,Rotroff Daniel M.¹,Judson Richard S.

Affiliation:

1. Bioinformatics Research Center, Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, United States

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci400527b

Reference68 articles.

1. Usingin VitroHigh Throughput Screening Assays to Identify Potential Endocrine-Disrupting Chemicals

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4. Developmental programming: Impact of fetal exposure to endocrine-disrupting chemicals on gonadotropin-releasing hormone and estrogen receptor mRNA in sheep hypothalamus

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