We introduce here ML4Tox, a framework offering Deep Learning and Support Vector Machine models to predict agonist, antagonist, and binding activities of chemical compounds, in this case for the estrogen receptor ligand-binding domain. The ML4Tox models have been developed with a 10 × 5-fold cross-validation schema on the training portion of the CERAPP ToxCast dataset, formed by 1677 chemicals, each described by 777 molecular features. On the CERAPP “All Literature” evaluation set (agonist: 6319 compounds; antagonist 6539; binding 7283), ML4Tox significantly improved sensitivity over published results on all three tasks, with agonist: 0.78 vs 0.56; antagonist: 0.69 vs 0.11; binding: 0.66 vs 0.26.
Machine learning models for predicting endocrine disruption potential of environmental chemicals / Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare. - In: JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS. - ISSN 1059-0501. - STAMPA. - 36:4(2019), pp. 237-251. [10.1080/10590501.2018.1537155]
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Titolo: | Machine learning models for predicting endocrine disruption potential of environmental chemicals | |
Autori: | Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare | |
Autori Unitn: | ||
Titolo del periodico: | JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS | |
Anno di pubblicazione: | 2019 | |
Numero e parte del fascicolo: | 4 | |
Codice identificativo Scopus: | 2-s2.0-85059908168 | |
Codice identificativo WOS: | WOS:000461662500005 | |
Digital Object Identifier (DOI): | http://dx.doi.org/10.1080/10590501.2018.1537155 | |
Handle: | http://hdl.handle.net/11572/343512 | |
Citazione: | Machine learning models for predicting endocrine disruption potential of environmental chemicals / Chierici, Marco; Giulini, Marco; Bussola, Nicole; Jurman, Giuseppe; Furlanello, Cesare. - In: JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS. - ISSN 1059-0501. - STAMPA. - 36:4(2019), pp. 237-251. [10.1080/10590501.2018.1537155] | |
Appare nelle tipologie: | 03.1 Articolo su rivista (Journal article) |