CogNet: Classification of Gene Expression Data based on ranked Active-Subnetwork-Oriented KEGG Pathway Enrichment Analysis

In this study we propose a new computational approach named CogNet that is based on biological knowledge as a function for grouping the genes for the task of ranks and classification. The pathfindR tool serves to be the biological grouping function allowing the main algorithm to rank active-subnetwork-oriented KEGG pathway enrichment analysis. Even Though, the main aim of the current tools is not improving results of any existing tools, the performance of the CogNet outperforms a similar approach called maTE while getting similar performance of other similar tools SVM-RCE. CogNet was tested on 13 gene expression datasets that include a variety of diseases.

CogNet provides a list of significant KEGG pathways including its genes that are able to separate the classes of the data. The list would serve the biology researcher for deep analysis and better interpretability of the role of KEGG pathways in the data or the case that is being studied. As a future work we would develop CogNet to explore the effectiveness of different combinations of the KEGG pathways in the data. In the current version we treat each KEGG pathway individually.

Find the full data set and documentation below in the "External resources" section.

External resources

• CogNet (paper) - PeerJ Computer Science
• CogNet - GitHub repository by 'malikyousef'
• CogNet (blog) - Malik Yousef Blog