Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards-Reference-Cited by-同舟云学术

Applications of Machine Learning to In Silico Quantification of Chemicals without Analytical Standards

Published:2020-05-07 Issue:6 Volume:60 Page:2718-2727
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Panagopoulos Abrahamsson Dimitri¹^ORCID,Park June-Soo²,Singh Randolph R.³,Sirota Marina⁴⁵,Woodruff Tracey J.¹

Affiliation:

1. Program on Reproductive Health and the Environment, Department of Obstetrics and Gynecology, University of California, San Francisco, California 94158, United States

2. Environmental Chemistry Laboratory, California Department of Toxic Substances Control, Berkeley, California 94710, United States

3. Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 4365 Esch-sur-Alzette, Luxembourg

4. Bakar Computational Health Sciences Institute, University of California, San Francisco, California 94158, United States

5. Department of Pediatrics, University of California, San Francisco, California 94158, United States

Funder

National Institute of Environmental Health Sciences

U.S. National Library of Medicine

U.S. Environmental Protection Agency

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/acs.jcim.9b01096

Reference41 articles.

1. The Exposome

2. Complementing the Genome with an "Exposome": The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology

3. Linking high resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring

4. Household Dust as a Repository of Chemical Accumulation: New Insights from a Comprehensive High-Resolution Mass Spectrometric Study

5. High-Throughput Models for Exposure-Based Chemical Prioritization in the ExpoCast Project

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