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

doi:10.3233/SHTI220385

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
Editors	Brigitte 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
Publisher	IOS Press BV
Pages	3-7
Number of pages	5
ISBN (Electronic)	9781643682846
DOIs	https://doi.org/10.3233/SHTI220385
Publication status	Published - May 25 2022
Event	32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France Duration: May 27 2022 → May 30 2022

Publication series

Name	Studies in Health Technology and Informatics
Volume	294
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	32nd Medical Informatics Europe Conference, MIE 2022
Country/Territory	France
City	Nice
Period	5/27/22 → 5/30/22

Bibliographical note

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/SHTI220385

Cite this

Majouni, S., Kim, J. S., Sweeney, E., Keltie, E., & Abidi, S. S. R. (2022). Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. In B. Seroussi, P. Weber, F. Dhombres, C. Grouin, J.-D. Liebe, J.-D. Liebe, J.-D. Liebe, S. Pelayo, A. Pinna, B. Rance, B. Rance, L. Sacchi, A. Ugon, A. Ugon, A. Benis, & P. Gallos (Eds.), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022 (pp. 3-7). (Studies in Health Technology and Informatics; Vol. 294). IOS Press BV. https://doi.org/10.3233/SHTI220385

Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. / Majouni, Sheida; Kim, Jong Sung ; Sweeney, Ellen et al.
Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. ed. / Brigitte 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. IOS Press BV, 2022. p. 3-7 (Studies in Health Technology and Informatics; Vol. 294).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Majouni, S, Kim, JS , Sweeney, E, Keltie, E & Abidi, SSR 2022, Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. in B Seroussi, P Weber, F Dhombres, C Grouin, J-D Liebe, J-D Liebe, J-D Liebe, S Pelayo, A Pinna, B Rance, B Rance, L Sacchi, A Ugon, A Ugon, A Benis & P Gallos (eds), Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. Studies in Health Technology and Informatics, vol. 294, IOS Press BV, pp. 3-7, 32nd Medical Informatics Europe Conference, MIE 2022, Nice, France, 5/27/22. https://doi.org/10.3233/SHTI220385

Majouni S, Kim JS , Sweeney E, Keltie E, Abidi SSR. Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. In Seroussi B, Weber P, Dhombres F, Grouin C, Liebe JD, Liebe JD, Liebe JD, Pelayo S, Pinna A, Rance B, Rance B, Sacchi L, Ugon A, Ugon A, Benis A, Gallos P, editors, Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. IOS Press BV. 2022. p. 3-7. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220385

Majouni, Sheida ; Kim, Jong Sung ; Sweeney, Ellen et al. / Applying Machine Learning to Arsenic Species and Metallomics Profiles of Toenails to Evaluate Associations of Environmental Arsenic with Incident Cancer Cases. Challenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022. editor / Brigitte 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. IOS Press BV, 2022. pp. 3-7 (Studies in Health Technology and Informatics).

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abstract = "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. ",

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

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

PubMed: MeSH publication types

UN SDGs

Access to Document

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