Classification of Breast Cancer Molecular Subtypes with Grouping-Scoring-Modeling Approach that Incorporates Disease-Disease Association Information

Qumsiyeh E., Bakir-Gungor B., Yousef M.

32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024, Mersin, Turkey, 15 - 18 May 2024 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu61531.2024.10601041
  • City: Mersin
  • Country: Turkey
  • Keywords: bioinformatics, biomarker discovery, disease-disease associations, feature selection methods, grouping-scoring-modeling (g-s-m), integrative approach, machine learning
  • Abdullah Gül University Affiliated: Yes

Abstract

This study uses modern sequencing technology and large biological databases to investigate the molecular intricacies of complicated diseases like cancer. Using gene expression databases and biomarkers, the research aims to improve breast cancer molecular subtype identification for better patient outcomes. Using BRCA LumAB_Her2Basal dataset, this study compares an integrative machine learning-based strategy (GediNET) to traditional feature selection approaches across machine learning classifiers. GediNET excels at uncovering crucial disease-disease connections and potential biomarkers using the Grouping-Scoring-Modeling (GSM) approach, which favors gene groupings above individual genes. Our comparative analysis highlights GediNET's exceptional performance, notably in terms of accuracy and Area Under the Curve metrics, underscoring its effectiveness in uncovering the genetic intricacies of breast cancer. GediNET's promise to improve disease classification and biomarker identification by improving biological mechanism understanding goes beyond exceeding traditional approaches. The work shows that GediNET's integrative method can promote bioinformatics research by identifying the most informative genes associated with certain diseases, enabling focused and customized medicine.