Machine Learning for Environmental Toxicology: A Call for Integration and Innovation

Author:

Miller Thomas H.1,Gallidabino Matteo D.2,MacRae James I.3,Hogstrand Christer4ORCID,Bury Nicolas R.5,Barron Leon P.1,Snape Jason R.67,Owen Stewart F.6

Affiliation:

1. Department of Analytical, Environmental & Forensic Sciences, School of Population Health & Environmental Sciences, Faculty of Life Sciences and Medicine, King’s College London, 150 Stamford Street, London SE1 9NH, U.K.

2. Department of Applied Sciences, Northumbria University, Newcastle Upon Tyne NE1 8ST, U.K.

3. Metabolomics Laboratory, The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, U.K.

4. Division of Diabetes and Nutritional Sciences, Faculty of Life Sciences and Medicine, King’s College London, Franklin Wilkins Building, 150 Stamford Street, London SE1 9NH, U.K.

5. School of Science, Technology and Engineering and Suffolk Sustainability Institute, University of Suffolk, James Hehir Building, University Avenue, Ipswich, Suffolk IP3 0FS, U.K.

6. AstraZeneca, Global Environment, Alderley Park, Macclesfield, Cheshire SK10 4TF, U.K.

7. School of Life Science, University of Warwick, Coventry CV4 7AL, U.K.

Funder

Medical Research Council

Wellcome Trust

Biotechnology and Biological Sciences Research Council

Seventh Framework Programme

Cancer Research UK

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

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