Published Tools

Introduction

EnGNet is a novel two-step method for large gene co-expression network inference. First, EnGNet uses an ensemble strategy for co-expression networks generation. Second, a greedy algorithm optimises both the size and the topological features of the network.

Implementation

This is a Java application for large gene co-expression network generation. As a multiplatform tools, it can be used in linux, mac, and windows.

In DataSets folder there are some input gene expression data for testing the tool.

Usage information

Turn to console and set:

> java -jar EnGNet.jar

Parameters (config.properties):

• inputFile: The path to the input dataset (gene expression matrix)
• KendallThreshold: threshold for the Kendall classifier
• SpearmanThreshold: threshold for the Spearman classifier
• NMIThreshold: threshold for the NMI classifier
• addingThreshold: threshold to determine if the edge would be return into the network after the pruning step
• hubThr: threshold to determine if the node studie is a hub. Set this threshold to -1 to run the algorithm with standard selection.

NOTE: You should have installed JAVA for running this tool.