Work

Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs)

Year: 2022

Type: article

Abstract: High Resolution Image Download MS PowerPoint Slide Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure–activity relationships for predicting possible bioactivities in a variety of PFAS species.

Source: Environmental Science & Technology Letters

DOI: 10.1021/acs.estlett.2c00530

Authors: Hyuna Kwon, Zulfikhar A. Ali, Bryan M. Wong

Institution: University of California, Riverside

Language: English

Topic: Per- and polyfluoroalkyl substances research

Subfield: Environmental Chemistry

Field: Environmental Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $5,000