PathNetDRP: a novel biomarker discovery framework using pathway and protein–protein interaction networks for immune checkpoint inhibitor response prediction

The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI response regulation. While these findings underscore the promise of PathNetDRP, future work will explore the integration of additional predictive features, such as tumor mutational burden and microsatellite instability, to further refine its applicability.

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Publication Details

Journal
BMC Bioinformatics
Published
2025-05-05
DOI
https://doi.org/10.1186/s12859-025-06125-0
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50

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article

PathNetDRP: a novel biomarker discovery framework using pathway and protein–protein interaction networks for immune checkpoint inhibitor response prediction

Jaegyoon Ahn, Dohee Lee, Jonghwan Choi
1 citations
BMC Bioinformatics
Computational Drug Discovery Methods
1.50
article

PathNetDRP: a novel biomarker discovery framework using pathway and protein–protein interaction networks for immune checkpoint inhibitor response prediction

Jaegyoon Ahn, Dohee Lee, Jonghwan Choi
article en
1 citations

Abstract

The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI response regulation. While these findings underscore the promise of PathNetDRP, future work will explore the integration of additional predictive features, such as tumor mutational burden and microsatellite instability, to further refine its applicability.

BMC BioinformaticsVol. 26(1)
Incheon National University (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion
Zero hunger
Openalex Percentile: Top 17%
Computational Drug Discovery Methods
1.50
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