Enhancing Breast Cancer Subtype Classification through GediNET: Integrating Disease- Disease Association Data with a Grouping-Scoring-Modeling Approach

Abstract

The development of sequencing technology and the increase in biological data repositories have allowed for a more thorough understanding of the complex molecular aspects of diseases like cancer. This paper evaluates GediNET, an integrative machine learning approach that employs a Grouping-Scoring-Modeling (GSM) approach to classify different molecular subtypes of breast cancer using the BRCA LumAB_Her2Basal dataset against different feature selection approaches and machine learning classifiers. GediNET distinguishes itself from traditional feature selection methods by analyzing groups of genes to identify relevant disease-disease associations and potential biomarkers. The results of our study show that GediNET performs better than traditional approaches in terms of accuracy and Area Under the Curve (AUC) metrics. This demonstrates that GediNET is effective in understanding the genetic intricacies of breast cancer. This approach improves the identification of molecular subtypes and promotes the development of targeted medicines and customized medicine.