Network-based disruption analysis of biological coordination

作者：Martin Becker, Dorien Feyaerts, Ina Annelies Stelzer, Amin Mirzaei, Eloïse Berson, Alan Lees Chang, Geetha Saarunya Clarke, Anthony Culos, Davide De Francesco, Camilo Espinosa, Yeasul Kim, Ivana Marić, Samson Mataraso, Seyedeh Neelufar Payrovnaziri, Thanaphong Phongpreecha, Neal G. Ravindra, Natalie Stanley, Sayane Shome, Yuqi Tan, Melan Thuraiappah, Lei Xue, Gary M. Shaw, David K. Stevenson, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour · 发表于：Patterns · 年份：2026 · DOI：10.1016/j.patter.2026.101618 · 研究领域：Bioinformatics and Genomic Networks、Gene Regulatory Network Analysis、Protein Structure and Dynamics

Biological systems comprise carefully coordinated processes. Challenges, e.g., pregnancy or surgery, can introduce major disruptions. Attempting to understand the corresponding complex adaptations, emerging technologies measure a multitude of biomarkers. However, current analytical tools often focus on changes in individual biomarkers and do not explicitly analyze the underlying, dynamically changing network of biomarker interactions. NeDis is an easy-to-use, highly customizable, open-source package that enables quantification of the functional disruption of biomarker networks across conditions and time. This allows the discovery of interconnected and coordinated subgroups of biomarkers with characteristic disruption profiles (e.g., increasing disruption over time) and provides a dynamic perspective on the coordination and adaptation of biological systems. Synthetic experiments demonstrate that NeDis captures more intricate signals than dimensionality reduction (e.g., principal-component analysis [PCA] or t-distributed stochastic neighbor embedding [t-SNE]). On high-dimensional single-cell mass cytometry (CyTOF) data, NeDis reveals coordinated functional disruptions of the immune system during human pregnancy, e.g., pointing to increased susceptibility to infection.