GOEGCN

"GOEGCN" (Gene Ontology Enrichment Based on Gene Coexpression Networks) is a computational method to enhance the understanding and identification of gene interaction mechanisms for different subtypes of breast invasive carcinoma (BRCA). This method integrates the biological importance of genes within gene regulatory networks with differential expression analysis to identify weighted differentially expressed genes (weighted DEGs), offering insights into the regulatory significance of these genes in the context of breast cancer subtypes.

Key Features and Functionalities:

- Integration of Gene Regulatory Networks and Differential Expression Analysis: GOEGCN uniquely combines insights from gene regulatory networks with differential expression analysis to identify weighted DEGs, highlighting genes with significant regulatory roles.

- Construction of Gene Coexpression Networks: The method constructs gene coexpression networks for control and experimental groups, identifying significantly differentially interacting structures within these networks.

- Gene Ontology (GO) Enrichment Analysis: GOEGCN performs GO enrichment analysis based on the constructed gene coexpression networks, allowing for a detailed examination of how changes in gene coexpression affect biological functions at the GO level.

- Two-Side Distinction Analysis: The method provides a two-side distinction analysis between gene coexpression networks for control and experimental groups, facilitating a deeper understanding of the specific biological function changes associated with each breast cancer subtype.

- Binary Classification for Subtype Identification: Using weighted DEGs, GOEGCN models binary classifiers for each breast cancer subtype, demonstrating the method's effectiveness in predicting unseen samples and validating the biological relevance of the proposed approaches.