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Updated: Oct 25, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
Published on: October 13, 2023
LipiDisease: associate lipids to diseases using literature mining
Piyush More1,2, Laura Bindila3, Philipp Wild4
1Department of Pharmacology, University Medical Center, 55131 Mainz, Germany.
Summary:
Lipids exhibit an essential role in cellular assembly and signaling. Dysregulation of these functions has been linked with many complications including obesity, diabetes, metabolic disorders, cancer and more. Investigating lipid profiles in such conditions can provide insights into cellular functions and possible interventions. Hence the field of lipidomics is expanding in recent years. Even though the role of individual lipids in diseases has been investigated, there is no resource to perform disease enrichment analysis considering the cumulative association of a lipid set. To address this, we have implemented the LipiDisease web server. The tool analyzes millions of records from the PubMed biomedical literature database discussing lipids and diseases, predicts their association and ranks them according to false discovery rates generated by random simulations. The tool takes into account 4270 diseases and 4798 lipids. Since the tool extracts the information from PubMed records, the number of diseases and lipids will be expanded over time as the biomedical literature grows.
Availability And Implementation:
The LipiDisease webserver can be freely accessed at http://cbdm-01.zdv.uni-mainz.de:3838/piyusmor/LipiDisease/.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
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