PLEX.I: a tool to discover features in multiplex networks that reflect clinical variation

Frontiers in Genetics

Genetics

Federico Melograna¹  Kristel Van Steen²  Benno Schwikowski³  Behnam Yousefi⁴  Farzaneh Firoozbakht⁵ 

[1] BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium;BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium;BIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Liège, Liège, Belgium;Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France;Computational Systems Biomedicine Lab, Institut Pasteur, Université Paris Cité, Paris, France;École Doctorale Complexite du Vivant, Sorbonne Université, Paris, France;BIO3—Laboratory for Systems Medicine, KU Leuven, Leuven, Belgium;Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany;

关键词: biological interaction networks;    functional genomics;    gene regulation;    machine learning;    software;   

DOI  :  10.3389/fgene.2023.1274637

 received in 2023-08-09, accepted in 2023-10-09,  发布年份 2023

来源: Frontiers

【 摘 要 】

Molecular profiling technologies, such as RNA sequencing, offer new opportunities to better discover and understand the molecular networks involved in complex biological processes. Clinically important variations of diseases, or responses to treatment, are often reflected, or even caused, by the dysregulation of molecular interaction networks specific to particular network regions. In this work, we propose the R package PLEX.I, that allows quantifying and testing variation in the direct neighborhood of a given node between networks corresponding to different conditions or states. We illustrate PLEX.I in two applications in which we discover variation that is associated with different responses to tamoxifen treatment and to sex-specific responses to bacterial stimuli. In the first case, PLEX.I analysis identifies two known pathways i) that have already been implicated in the same context as the tamoxifen mechanism of action, and ii) that would have not have been identified using classical differential gene expression analysis.

【 授权许可】

Unknown   
Copyright © 2023 Yousefi, Firoozbakht, Melograna, Schwikowski and Van Steen.