This repository contains several notebooks that are designed to demonstrate the effect of different strategies of negative example selection on the measured performance of graph representation machine learning in which the nodes of a knowledge graph are embedded into a low-dimensional vector space, transformed by approaches such as the Hadamard transformation into edge representations, following which edge classification is performed by methods such as Random Forest or perceptron classification.

Graph operations and machine learning are performed using GRAPE.

A minimum of Python version 3.8 is required. The easiest way to run the notebooks is to set up a virtual environment as follows.

python3 -m venv venv
source venv/bin/activate
pip install pandas numpy grape barplots
pip install networkx # required for the SliNetwork notebook only
pip install powerlaw # required for the SliNetwork notebook only
pip install plotnine # required for some graphics
pip install jupyter
python -m ipykernel install --user --name="venv"

Following this, start jupyter or jupyter-notebook and run any of the notebooks in this directory.

Biomedical knowledge graphs include edges representing various types of relations between biological entities such as protein-protein interac-tions, synthetic lethality, or shared biological functions. Because our knowledge about explicit negative edges, i.e., pairs of nodes that were demonstrated not to have a specific relation, negative examples are generally sampled at random from pairs of nodes that are not positively labeled under the assumption that the majority of non-labelled pairs are negative.

• SliNetwork: Characterization of the Synthetic Lethality Interation (SLI) network.
• KG-IDG Network: Characterization of KG-IDG (Illuminating the Druggable Genome) graph.

In this experiment, we show that it is possible to obtain respectable classification performance by using solely the degree of both nodes of an edge as features.

• degreeBias: Classification of the SLI network based on node degree.

We ran one analysis on a SLURM cluster that performed analysis with different classifiers. We present the SLURM script we used as well as the python script in the scripts subdirectory. Running these scripts will generate a folder called experiments with all the results of analysis. The following notebook can be used to ingest these files and calculate mean and standard deviations for the various models and parameters.

• extractingResults.

We have copied the output of the extractingResults notebook into the results folder. R script are provided to generate several of the figures in the manuscript.

The learning algorithms used for this analysis were implemented in GRAPE and the default parameters were used. The following script creates a table with these parameters for convenience.

• modelParameterTable: Extract and summarize parameters.