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

URL:	https://ceur-ws.org/Vol-3726/paper2.pdf
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2024
Buchtitel:	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)
Titel einer Zeitschrift oder einer Reihe:	CEUR Workshop Proceedings
Band/Volume:	3726
Seitenbereich:	1-11
Veranstaltungstitel:	Workshop on Semantic Web solutions for large-scale biomedical data analytics
Veranstaltungsort:	Heraklion
Veranstaltungsdatum:	26.05.2024
Herausgeber:	Hasnain, Ali ; Tirado, Alba Morales ; Dumontier, Michel ; Rebholz-Schuhmann, Dietrich
Ort der Veröffentlichung:	Aachen, Germany
Verlag:	RWTH Aachen
ISSN:	1613-0073
Verwandte URLs:	https://ceur-ws.org/Vol-3726/
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Data Science (Paulheim 2018-)
Fachgebiet:	004 Informatik
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.