Inference of differential gene regulatory networks using boosted differential trees

Galindez G, List M, Baumbach J, Völker U, Mäder U, Blumenthal DB, Kacprowski T (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Bioinformatics Advances Oxford University Press

Book Volume: 4

Article Number: vbae034

Journal Issue: 1

DOI: 10.1093/bioadv/vbae034

Abstract

Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses.

Authors with CRIS profile

David Blumenthal Professur für Biomedical Network Science

Involved external institutions

Universitätsmedizin Greifswald / Universitätsklinikum Greifswald DE Germany (DE) Peter L. Reichertz Institut für Medizinische Informatik (PLRI) DE Germany (DE) Technische Universität München (TUM) DE Germany (DE) Universität Hamburg (UHH) DE Germany (DE)

How to cite

APA:

Galindez, G., List, M., Baumbach, J., Völker, U., Mäder, U., Blumenthal, D.B., & Kacprowski, T. (2024). Inference of differential gene regulatory networks using boosted differential trees. Bioinformatics Advances, 4(1). https://doi.org/10.1093/bioadv/vbae034

MLA:

Galindez, Gihanna, et al. "Inference of differential gene regulatory networks using boosted differential trees." Bioinformatics Advances 4.1 (2024).

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