Protein Graph Library

Code for ICLR 2024 (Spotlight) paper "MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding"

A Graph Neural Network Model for prediction of the effectiveness of a drug on a given cancer cell lines

Structured Multi-task Learning for Molecular Property Prediction, AISTATS'22 (https://proceedings.mlr.press/v151/liu22e.html)

Ensemble learning with graph neural networks for disease module discovery and classification

Python 3 library implementing a number of topological clustering techniques used on protein-protein interaction networks.

A PPI network driven approach to drug-target-interaction prediction using deep graph learning methods.

Embeddings to Network Alignment - align biological networks between two species

Codes for the paper entitled: Non-Coding RNAs Improve the Predictive Power of Network Medicine

Gene prioritisation tool using a weighted network methodology based on Gene Ontology and Human Phenotype Ontology annotations to infer closely related genes to given genes of interest.

Graph representation for system biology networks and the reduction of them for machine learning purposes

A tool for calculating PPI network rewiring due to alternative splicing

New implementation of the Girvan-Newman edge-betweenness' algorithm and study of the clustering structure through modularity. Based on "A MapReduce-Based Parallel Clustering Algorithm for Large Protein-Protein Interaction Networks", by Liu and others

Evaluate a strategy for predicting cancer-related proteins in PPI networks.

Opportunities and challenges in partitioning the graph measure space of real-world networks