Integrating structure annotation and machine learning approaches to develop graphene toxicity models.

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Bibliographic Details
Title: Integrating structure annotation and machine learning approaches to develop graphene toxicity models.
Authors: Wang T; Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA., Russo DP; Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA., Bitounis D; Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA.; Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA., Demokritou P; Center for Nanotechnology and Nanotoxicology, Department of Environmental Health, T.H. Chan School of Public Health, Harvard University, 655 Huntington Ave, Boston, MA 02115, USA.; Nanoscience and Advanced Materials Center, Environmental Occupational Health Sciences Institute, School of Public Health, Rutgers University, Piscataway, New Jersey 08854, USA., Jia X; Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA., Huang H; Department of Electrical and Computer Engineering, Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Boulevard, Pittsburgh, Pennsylvania, USA., Zhu H; Department of Chemistry and Biochemistry, Rowan University, Glassboro, NJ 08028, USA.
Source: Carbon [Carbon N Y] 2023 Feb; Vol. 204, pp. 484-494. Date of Electronic Publication: 2022 Dec 26.
Publication Type: Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 101204394 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0008-6223 (Print) Linking ISSN: 00086223 NLM ISO Abbreviation: Carbon N Y Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:0008-6223
DOI:10.1016/j.carbon.2022.12.065