XGBfnc 0.1.3

Flat, node classification model

• Written by Miguel Romero

• Last update: 01/07/21

Classification of nodes with structural properties

This package aims to evaluate whether the structural (topological) properties of a network are useful for predicting node attributes of nodes (i.e., node classification). It uses a combination of multiple machine learning techniques, such as, XGBoost and the SMOTE sampling technique.

Installation

The xgbfnc package can be install using pip, the requirements will be automatically installed:

python3 -m pip install XGBfnc

The source code and examples can be found in the GitHub repository.

Documentation

Documentation of the package can be found here.

Example

The example illustrates how the algorithm can be used to check whether the structural properties of the gene co-expression network improve the performance of the prediction of gene functions for rice (Oryza sativa Japonica). In this example, a gene co-expression network gathered from ATTED II is used.

How to run the example?