Integration of annotated phenotype, gene and chemical text data to advance exposome informatics

Christopher Hawthorne; Guillermo H Lopez-Campos

doi:10.3233/SHTI220611

Integration of annotated phenotype, gene and chemical text data to advance exposome informatics

Christopher Hawthorne, Guillermo H Lopez-Campos

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

1 Citation (Scopus)

45 Downloads (Pure)

Abstract

The field of phenomics has a range of biomedical informatics tools such as the Human Phenotype Ontology, providing a structured vocabulary with relationships between abnormal phenotype terms. Artificial intelligence has been widely used for entity extraction and tagging large corpora of text from PubMed and is reflected in applications such as PheneBank and PubTator. Phexpo is a tool for predicting chemical - phenotype relationships and vice-versa, although lacks the ability to decipher known relationships from unknown. Integration of these three resources can provide new meaningful relationships between phenotypes, genes and chemicals and has yet to be fully leveraged. Here we present a methodology to construct two new datasets for phenotype - gene and phenotype - chemical relationships and showcase how these datasets can be used to enhance exposome informatics.

Original language	English
Title of host publication	Challenges of trustable AI and added-value on health
Editors	Brigitte Séroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Lucia Sacchi, Adrien Ugon, Arriel Benis, Parisis Gallos
Publisher	IOS Press
Pages	870-871
Number of pages	2
ISBN (Electronic)	9781643682853
ISBN (Print)	9781643682846
DOIs	https://doi.org/10.3233/SHTI220611
Publication status	Published - 25 May 2022

Publication series

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

Keywords

Artificial Intelligence
Data Mining/methods
Exposome
Phenotype
PubMed

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10.3233/SHTI220611Licence: CC BY-NC

Integration of annotated phenotype, gene and chemical text data to advance exposome informatics
© 2022 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0)
Final published version, 106 KBLicence: CC BY-NC

Investigation and development of novel exposome informatics methodologies and solutions for the analysis and integration of phenome, genome and exposome data
Hawthorne, C. (Author), Lopez Campos, G. (Supervisor), Simpson, D. (Supervisor) & Devereux, B. (Supervisor), Dec 2023
Student thesis: Doctoral Thesis › Doctor of Philosophy

Cite this

Hawthorne, C., & Lopez-Campos, G. H. (2022). Integration of annotated phenotype, gene and chemical text data to advance exposome informatics. In B. Séroussi, P. Weber, F. Dhombres, C. Grouin, J.-D. Liebe, S. Pelayo, A. Pinna, B. Rance, L. Sacchi, A. Ugon, A. Benis, & P. Gallos (Eds.), Challenges of trustable AI and added-value on health (pp. 870-871). (Studies in Health Technology and Informatics; Vol. 294). IOS Press. https://doi.org/10.3233/SHTI220611

Hawthorne, Christopher ; Lopez-Campos, Guillermo H. / Integration of annotated phenotype, gene and chemical text data to advance exposome informatics. Challenges of trustable AI and added-value on health. editor / Brigitte Séroussi ; Patrick Weber ; Ferdinand Dhombres ; Cyril Grouin ; Jan-David Liebe ; Sylvia Pelayo ; Andrea Pinna ; Bastien Rance ; Lucia Sacchi ; Adrien Ugon ; Arriel Benis ; Parisis Gallos. IOS Press, 2022. pp. 870-871 (Studies in Health Technology and Informatics).

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Hawthorne, C & Lopez-Campos, GH 2022, Integration of annotated phenotype, gene and chemical text data to advance exposome informatics. in B Séroussi, P Weber, F Dhombres, C Grouin, J-D Liebe, S Pelayo, A Pinna, B Rance, L Sacchi, A Ugon, A Benis & P Gallos (eds), Challenges of trustable AI and added-value on health. Studies in Health Technology and Informatics, vol. 294, IOS Press, pp. 870-871. https://doi.org/10.3233/SHTI220611

Integration of annotated phenotype, gene and chemical text data to advance exposome informatics. / Hawthorne, Christopher; Lopez-Campos, Guillermo H.
Challenges of trustable AI and added-value on health. ed. / Brigitte Séroussi; Patrick Weber; Ferdinand Dhombres; Cyril Grouin; Jan-David Liebe; Sylvia Pelayo; Andrea Pinna; Bastien Rance; Lucia Sacchi; Adrien Ugon; Arriel Benis; Parisis Gallos. IOS Press, 2022. p. 870-871 (Studies in Health Technology and Informatics; Vol. 294).

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

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T1 - Integration of annotated phenotype, gene and chemical text data to advance exposome informatics

AU - Hawthorne, Christopher

AU - Lopez-Campos, Guillermo H

PY - 2022/5/25

Y1 - 2022/5/25

N2 - The field of phenomics has a range of biomedical informatics tools such as the Human Phenotype Ontology, providing a structured vocabulary with relationships between abnormal phenotype terms. Artificial intelligence has been widely used for entity extraction and tagging large corpora of text from PubMed and is reflected in applications such as PheneBank and PubTator. Phexpo is a tool for predicting chemical - phenotype relationships and vice-versa, although lacks the ability to decipher known relationships from unknown. Integration of these three resources can provide new meaningful relationships between phenotypes, genes and chemicals and has yet to be fully leveraged. Here we present a methodology to construct two new datasets for phenotype - gene and phenotype - chemical relationships and showcase how these datasets can be used to enhance exposome informatics.

AB - The field of phenomics has a range of biomedical informatics tools such as the Human Phenotype Ontology, providing a structured vocabulary with relationships between abnormal phenotype terms. Artificial intelligence has been widely used for entity extraction and tagging large corpora of text from PubMed and is reflected in applications such as PheneBank and PubTator. Phexpo is a tool for predicting chemical - phenotype relationships and vice-versa, although lacks the ability to decipher known relationships from unknown. Integration of these three resources can provide new meaningful relationships between phenotypes, genes and chemicals and has yet to be fully leveraged. Here we present a methodology to construct two new datasets for phenotype - gene and phenotype - chemical relationships and showcase how these datasets can be used to enhance exposome informatics.

KW - Artificial Intelligence

KW - Data Mining/methods

KW - Exposome

KW - Phenotype

KW - PubMed

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BT - Challenges of trustable AI and added-value on health

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A2 - Weber, Patrick

A2 - Dhombres, Ferdinand

A2 - Grouin, Cyril

A2 - Liebe, Jan-David

A2 - Pelayo, Sylvia

A2 - Pinna, Andrea

A2 - Rance, Bastien

A2 - Sacchi, Lucia

A2 - Ugon, Adrien

A2 - Benis, Arriel

A2 - Gallos, Parisis

PB - IOS Press

ER -

Hawthorne C, Lopez-Campos GH. Integration of annotated phenotype, gene and chemical text data to advance exposome informatics. In Séroussi B, Weber P, Dhombres F, Grouin C, Liebe JD, Pelayo S, Pinna A, Rance B, Sacchi L, Ugon A, Benis A, Gallos P, editors, Challenges of trustable AI and added-value on health. IOS Press. 2022. p. 870-871. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI220611

Integration of annotated phenotype, gene and chemical text data to advance exposome informatics

Abstract

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Student theses

Investigation and development of novel exposome informatics methodologies and solutions for the analysis and integration of phenome, genome and exposome data

Cite this