BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus

Agnete T Lundgaard; Frédéric Burdet; Troels Siggaard; David Westergaard; Danai Vagiaki; Lisa Cantwell; Timo Röder; Dorte Vistisen; Thomas Sparsø; Giuseppe N Giordano; Mark Ibberson; Karina Banasik; Søren Brunak

doi:10.1371/journal.pcbi.1011403

BALDR: A Web-based platform for informed comparison and prioritization of biomarker candidates for type 2 diabetes mellitus

PLoS Comput Biol. 2023 Aug 17;19(8):e1011403. doi: 10.1371/journal.pcbi.1011403. eCollection 2023 Aug.

Authors

Affiliations

¹ Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Blegdamsvej 3B, Copenhagen, Denmark.
² Vital-IT, Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
³ Clinical Epidemiological Research, Steno Diabetes Center Copenhagen, Herlev, Denmark.
⁴ Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
⁵ Bioinformatics and Data Mining, Global Research Technologies, Novo Nordisk A/S, Måløv, Denmark.
⁶ Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre, Lund University, Skåne University Hospital, Malmö, Sweden.

Abstract

Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website https://baldr.cpr.ku.dk.

Copyright: © 2023 Lundgaard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Biomarkers
Data Mining
Diabetes Mellitus, Type 2*
Humans
Internet
Mice
Pandemics

Substances

Biomarkers

Grants and funding

Funding for the work performed has been provided within the framework of the RHAPSODY Consortium. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115881 (RHAPSODY). RHAPSODY receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This work is supported by the Swiss State Secretariat for Education‚ Research and Innovation (SERI) under contract number 16.0097. The opinions expressed and arguments employed herein do not necessarily reflect the official views of these funding bodies. Furthermore, this work were supported by the Novo Nordisk Foundation (grants NNF14CC0001 and NNF17OC0027594 to A.T.L., T.Si., D.W., D.Va., L.C., T.R., K.B., and S.B.), Bayer A/S (research grants to D.Vi.), Sanofi Aventis (research grants to D.Vi.), Novo Nordisk A/S (research grants to D.Vi.), and Boehringer Ingelheim (research grants to D.Vi.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.