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Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning
Ghazaleh Khalili-Tanha1, Alireza Shoari2
1Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad 91388-13944, Iran.
This review highlights how artificial intelligence and advanced technology can identify new breast cancer biomarkers linked to epithelial-mesenchymal transition (EMT). Discovering these biomarkers improves early detection and treatment planning for breast cancer patients.
Area of Science:
- Oncology
- Biomedical Engineering
- Computational Biology
Background:
- Breast cancer presents diagnostic and therapeutic challenges due to its heterogeneity.
- Precision medicine seeks novel biomarkers for enhanced early detection, prognosis, and treatment.
- Epithelial-mesenchymal transition (EMT) is a key process in cancer progression.
Purpose of the Study:
- To review the role of cutting-edge technology and artificial intelligence (AI) in identifying novel biomarkers.
- To focus on biomarkers associated with epithelial-mesenchymal transition (EMT) in breast cancer.
- To explore how multi-omics data analysis can advance breast cancer therapeutics.
Main Methods:
- Review of existing clinical and preclinical studies on breast cancer.
- Emphasis on the application of artificial intelligence (AI) and statistical analysis.
- Integration of multi-omics data for biomarker discovery.
Main Results:
- AI and advanced technologies are crucial for identifying new breast cancer biomarkers.
- Biomarkers associated with EMT are critical for understanding cancer progression.
- Statistical and machine learning methods applied to multi-omics data facilitate novel biomarker discovery.
Conclusions:
- Identifying EMT-related biomarkers through AI and multi-omics data analysis is vital for advancing breast cancer treatment.
- This approach supports precision medicine strategies for improved patient outcomes.
- Further research integrating technology and data science will enhance therapeutic development.
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