Integrating heterogeneous gene expression data through knowledge graphs for improving diabetes prediction

URL:	https://ceur-ws.org/Vol-3726/paper2.pdf
Document Type:	Conference or workshop publication
Year of publication:	2024
Book title:	SeWeBMeDa 2024 : Proceedings of the 7th Workshop on Semantic Web solutions for large-scale biomedical data analytics co-located with the ESWC 2024: Extended Semantic Web Conference (ESWC 2024)
The title of a journal, publication series:	CEUR Workshop Proceedings
Volume:	3726
Page range:	1-11
Conference title:	Workshop on Semantic Web solutions for large-scale biomedical data analytics
Location of the conference venue:	Heraklion
Date of the conference:	26.05.2024
Publisher:	Hasnain, Ali ; Tirado, Alba Morales ; Dumontier, Michel ; Rebholz-Schuhmann, Dietrich
Place of publication:	Aachen, Germany
Publishing house:	RWTH Aachen
ISSN:	1613-0073
Related URLs:	https://ceur-ws.org/Vol-3726/
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
Subject:	004 Computer science, internet
Abstract:	Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of diverse data types, namely gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the sample sizes in expression datasets are usually limited, and the data from different datasets with different gene expressions cannot be easily combined. This work proposes a novel approach to address these challenges by integrating multiple gene expression datasets and domain-specific knowledge using knowledge graphs, a unique tool for biomedical data integration. KG embedding methods are then employed to generate vector representations, serving as inputs for a classifier. Experiments demonstrated the efficacy of our approach, revealing improvements in diabetes prediction when integrating multiple gene expression datasets and domain-specific knowledge about protein functions and interactions.