Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases

Sheida Majouni, Jong Sung Kim, Ellen Sweeney, Erin Keltie, Syed Sibte Raza Abidi

Résultat de recherche: Conference contribution

2 Citations (Scopus)

Résumé

Chronic exposure to environmental arsenic has been linked to a number of human diseases affecting multiple organ systems, including cancer. The greatest concern for chronic exposure to arsenic is contaminated groundwater used for drinking as it is the main contributor to the amount of arsenic present in the body. An estimated 40% of households in Nova Scotia (Canada) use water from private wells, and there is a concern that exposure to arsenic may be linked to/associated with cancer. In this preliminary study, we are aiming to gain insights into the association of environmental metal's pathogenicity and carcinogenicity with prostate cancer. We use toenails as a novel biomarker for capturing long-term exposure to arsenic, and have performed toxicological analysis to generate data about differential profiles of arsenic species and the metallome (entirety of metals) for both healthy and individuals with a history cancer. We have applied feature selection and machine learning algorithms to arsenic species and metallomics profiles of toenails to investigate the complex association between environmental arsenic (as a carcinogen) and prostate cancer. We present machine learning based models to ultimately predict the association of environmental arsenic exposure in cancer cases.

Langue d'origineEnglish
Titre de la publication principaleChallenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
ÉditeursBrigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Jan-David Liebe, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Bastien Rance, Lucia Sacchi, Adrien Ugon, Adrien Ugon, Arriel Benis, Parisis Gallos
Maison d'éditionIOS Press BV
Pages3-7
Nombre de pages5
ISBN (électronique)9781643682846
DOI
Statut de publicationPublished - mai 25 2022
Événement32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France
Durée: mai 27 2022mai 30 2022

Séries de publication

PrénomStudies in Health Technology and Informatics
Volume294
ISSN (imprimé)0926-9630
ISSN (électronique)1879-8365

Conference

Conference32nd Medical Informatics Europe Conference, MIE 2022
Pays/TerritoireFrance
VilleNice
Période5/27/225/30/22

Note bibliographique

Funding Information:
This research was conducted using Atlantic PATH data and biosamples with funding from the NS Health Research Fund and the Canadian Cancer Society/New Brunswick Health Research Foundation. The data used in this research were made available by the Atlantic PATH study, which is the Atlantic Canada regional component of the CanPath funded by the Canadian Partnership Against Cancer and Health Canada. The views expressed herein represent the views of the authors and do not necessarily represent the views of Health Canada.

Publisher Copyright:
© 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

ASJC Scopus Subject Areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

PubMed: MeSH publication types

  • Journal Article

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