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OntoMerger is an ontology alignment library for deduplicating knowledge graph nodes that represent the same domain.
Unified knowledge-driven network inference from omics data
Molecular interactions inference from single-cell multi-omics data
The CoLoMoTo Interactive Notebook: Accessible and Reproducible Computational Analyses for Qualitative Biological Networks
Explore biomolecular pathways in Reactome from the command-line or a Python script
A network based gene classification library to generate genome wide predictions about genes that are functionally similar to the input gene list.
MGN-Net: A novel Graph Neural Network for integrating heterogenous graph population derived from multiple sources.
This repository includes Matlab codes/routines that were used in our manuscript entitled "Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks" that can be found in this preprint: https://arxiv.org/abs/1911.06286
Visualisation and Analysis of Networks conTaining Experimental Data
R Package: Steiner tree approach for graph analysis
Evolution of protein interactomes across the tree of life
3D Spiking neural network simulation exploring Spike Timing Dependent Plasticity (STDP)
Exploring multicellular coordination from single-cell gene expression / multi-omics using mutlilayer network representations
Mutual Interactors: A graph-based machine learning model with applications in molecular phenotype prediction
PathwayMatcher is a free, open-source software to search for pathways related to a list of proteins in Reactome. This repository is the development repository. For the latest version see: https://github.com/PathwayAnalysisPlatform/PathwayMatcher
Awesome list of research publications and media on biologically-motivated learning algorithms.
Modeling and simulation of biological networks using automatic transformation to Petri nets (HFPN / xHPN)
⌘ | Develops emergent intelligence through the sum (or harmony) of it's parts.
Translation between CellDesigner-flavoured SBML and SBGN-ML
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