Decision Tree for Protein Biomarker Selection for Clinical Applications

Katharina Waury

doi:10.1007/978-1-0716-4298-6_21

Decision Tree for Protein Biomarker Selection for Clinical Applications

Katharina Waury

Computer Science

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

Abstract

Discovery of novel protein biomarkers for clinical applications is an active research field across a manifold of diseases. Despite some successes and progress, the biomarker development pipeline still frequently ends in failure. Biomarker candidates that are discovered by appropriate technologies such as unbiased mass spectrometry cannot be validated or translated to immunoassays in many cases. Selection of strong disease biomarker candidates that further constitute suitable targets for antibody binding in immunoassays is thus important to allow routine clinical use. This essential selection step can be supported and rationalized using bioinformatics tools such as protein databases. Here, we present a workflow in the form of decision trees to computationally investigate biomarker candidates and their available affinity reagents in depth. This analysis can identify the most promising biomarker candidates for assay development, while minimal time and effort are required.

Original language	English
Title of host publication	Tissue Proteomics
Subtitle of host publication	Methods and Protocols
Editors	Taufika Islam Williams
Publisher	Springer Nature
Pages	355-368
Number of pages	14
ISBN (Electronic)	9781071642986
ISBN (Print)	9781071642979, 9781071643006
DOIs	https://doi.org/10.1007/978-1-0716-4298-6_21
Publication status	Published - 2025

Publication series

Name	Methods in molecular biology (Clifton, N.J.)
Publisher	Springer
Volume	2884
ISSN (Print)	1064-3745

Bibliographical note

Publisher Copyright:
© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Funding

This work is supported by the European Union\u2019s Horizon 2020 research and innovation program under the Marie Sk\u0142odowskaCurie grant agreement No. 860197, the MIRIADE project. The author would like to acknowledge the fellow researchers from the MIRIADE consortium who provided valuable insights and interesting discussions regarding fluid biomarker development over the years.

Funders	Funder number
Horizon 2020 Framework Programme	860197

Keywords

Antibodies
Bioinformatics
Immunoassays
Online databases
Protein biomarker

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10.1007/978-1-0716-4298-6_21

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KW - Online databases

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Decision Tree for Protein Biomarker Selection for Clinical Applications

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