Machine-learning methods in detecting breast cancer and related therapeutic issues: a review

Jafari, Ali

doi:10.1080/21681163.2023.2299093

Cited by 2 publications

(2 citation statements)

References 76 publications

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“…So, the utilization of ML algorithms such as DNN, LR, RF, SVM, KNN, and DT is extensively recognized in breast cancer research. Some of the traditional methods including Cox regression models, HLM, differential analysis, Mann Whitney U test, t-test, and ANOVA, continue to be pivotal for analyzing the diagnostic patterns of breast cancer [197] , [198] . Analyzing microarray datasets with ML/DL algorithms can help pinpoint key protein biomarkers for early pancreatic cancer detection.…”

Section: Discussionmentioning

confidence: 99%

Methods in DNA methylation array dataset analysis: A review

Sahoo,

Sundararajan

2024

Computational and Structural Biotechnology Journal

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Section: Discussionmentioning

confidence: 99%

Methods in DNA methylation array dataset analysis: A review

Sahoo,

Sundararajan

2024

Computational and Structural Biotechnology Journal

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“…Breast cancer is a complicated disease that needs to be properly diagnosed and categorized in order to plan effective treatment (1). In recent years, machine learning methods have become useful tools in breast cancer research, with the potential to improve diagnosis accuracy and make personalized therapies possible (2). One area of interest is the identi cation and description of types of breast cancer based on how receptors like HER2-and ER+ are expressed.…”

Section: Literature Reviewmentioning

confidence: 99%

Devising Breast Cancer Diagnosis Protocol through Machine Learning

Mujtaba

2024

Preprint

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Breast cancer is a life threatening disease and have serious implications to health. It is further categorized on the bases of receptors including ER+ and HER2-. Breast cancer is a multifaceted disease that has many subcategories characterized by unique genetic features. This research focuses on two important subgroups of receptors, including ER+ and HER2-. We conducted an analysis of gene expression data obtained from reliable source (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. 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.

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Machine-learning methods in detecting breast cancer and related therapeutic issues: a review

Cited by 2 publications

References 76 publications

Methods in DNA methylation array dataset analysis: A review

Methods in DNA methylation array dataset analysis: A review

Devising Breast Cancer Diagnosis Protocol through Machine Learning

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