GediNET- Discovering Multi-Disease Gene Associations using Established Biological Information and Knowledge-based Machine Learning

Emma Qumsiyeh^1*, Louise Showe², and Malik Yousef^3,4*
1Information Technology Engineering, Al-Quds University, Palestine
²The Wistar Institute, Philadelphia, PA,19104, USA
³Department of Information Systems, Zefat Academic College, Zefat, 13206, Israel
⁴Galilee Digital Health Research Center (GDH), Zefat Academic College, Israel
^*Corresponding authors:
Malik Yousef: malik.yousef@gmail.com
Emma Qumsiyeh: emma.qumsiyeh@hotmail.com

• Correspondence: malik.yousef@gmail.com;

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Knime Workflow

GediNET tool is a Knime workflow. In order to run the workflow, you need to download Knime and install it in your local machine. This is the link for downloading Knime: https://www.knime.com/downloads
For more information about the Knime platform you might visit https://www.knime.com/software-overview
See this page for information about setting Knime.
Visit this page for instruction in how to prepare the dataset into Knime table format (*.table) using a Knime workflow
Visit this page for instruction in how to upload the Groups file.
Visit this page for the outputs of GediNET.
The Knime workflow name is "GediNET_v1.knwf" that you might download and run throug the Knimeplatform

Running the workflow:

• You need to use the node “MCCV Iterations” in order to specify the number of Monte Carlo Cross Validation (MCCV) iterations, for example 10 or 100.
• You need to configure the node “List Files/Folders” to point it to the folder that has the gene expression dataset in a table format (as described above)
• You might download an example of such data named DSD84.table
  

The Pseudocode of GediNET can seen over this file

GediNET main workflow is:

The content of the MetaNode GediNET is :

The content of the G-S-M component

The 3 main component of G-S-M