Article
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Devising Breast Cancer Diagnosis Protocol through Machine Learning
Version 1
: Received: 2 February 2024 / Approved: 5 February 2024 / Online: 5 February 2024 (15:21:45 CET)
How to cite: Mujtaba, T. Devising Breast Cancer Diagnosis Protocol through Machine Learning. Preprints 2024, 2024020260. https://doi.org/10.20944/preprints202402.0260.v1 Mujtaba, T. Devising Breast Cancer Diagnosis Protocol through Machine Learning. Preprints 2024, 2024020260. https://doi.org/10.20944/preprints202402.0260.v1
Abstract
Breast cancer is a multifaceted disease that has many subcategories characterized by unique genetic features. This research focuses on two important subgroups, including ER+ and HER2-. We conducted an analysis of gene expression data obtained from reliable sources (Array Express: E-GEOD-52194, E-GEOD-75367, and E-GEOD-58135) in order to reveal the complex molecular details of these subtypes. The computational pipeline we used identified 396 genes that exhibited distinct patterns of gene expression in ER+ and HER2- breast cancers. The diagnostic and prognostic significance of these genes was evaluated using machine learning methods, namely SVM and decision tree models. Metrics like as accuracy, sensitivity, and specificity provide insights into their usefulness. Furthermore, the use of the STRING database for network analysis revealed significant signaling pathways and biological processes associated with the development of ER+ and HER2- breast cancer. The results of our research enhance our comprehension of these subcategories, which might possibly facilitate more accurate diagnoses and focused treatment interventions. To summarize, this work provides valuable information on the genetic foundations of ER+ and HER2- breast cancer, which has potential implications for enhancing patient treatment and outcomes.
Keywords
Estrogen Receptor; Progestrone Receptor; Human epidermal growth factor Receptor
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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