EvoKEN: evolutionary knowledge extraction in networks
Benjamin Linard, Ngoc Hoan Nguyen, Odile Lecompte, Olivier Poch, Julie D. Thompson
Abstract
We introduce a multi-factorial, multi-level approach to build and explore evolutionary scenarios of complex protein networks. EvoKEN combines a unique formalism for integrating multiple types of data associated with network molecular components and knowledge extraction techniques for detecting cohesive/anomalous evolutionary processes. We analyzed known human pathway maps and identified perturbations or specializations at the local topology level that reveal important evolutionary and functional aspects of these cellular systems.
02 /Specification & Classification
Click any tag to browse matching papers- Organism
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Identification of co-evolving temporal networks
Rasha Elhesha, Aisharjya Sarkar, … Tamer Kahveci·BMC GenomicsPeer-reviewed·Jun 1, 2019
- Deciphering biological evolution exploiting the topology of Protein Locality Graph
B.N. Das, Pralay Mitra·bioRxiv·Jun 3, 2021
- Correlated Evolution of Metabolic Functions over the Tree of Life
Murray Patterson, Thomas Bernard, Daniel Kahn·bioRxiv·Dec 13, 2016
- MolEvolvR: A web-app for characterizing proteins using molecular evolution and phylogeny
Faisal Alquaddoomi, Joseph T Burke, … Janani Ravi·bioRxiv·Feb 18, 2022
- CoEVFold suite: user friendly pipelines to visually represent protein coevolution
Chris LB Graham, Liam Cremona, … Christopher D. A. Rodrigues·bioRxiv·Jan 26, 2026
- Selection on network dynamics drives differential rates of protein domain evolution
Brian K Mannakee, Ryan N Gutenkunst·bioRxiv·Sep 11, 2015