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

Network Regression Embeddings reveal cell-type Transcription Factor coordination for target gene (TG) regulation

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.

A tool for calculating PPI network rewiring due to alternative splicing

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

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

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

Downstream Analysis of RNA-Seq Results in R: GSEA, PPI Networks, and Biological Interpretation

[R] Generating Protein-Protein Interaction Networks using R and Quarto publishing system in HTML format.

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