Understanding genetic data: The advancing development of clinical stratification and precision oncology through machine learning
The human genome project and subsequent projects, including the HapMap and 1000 Genomes, have laid the foundation for precision medicine, an approach tailoring treatments to patients. Precision oncology, particularly in breast cancer, applies precision medicine by analyzing a tumor’s genomic makeup to guide treatment. Single-nucleotide polymorphisms (SNPs) contribute to both breast cancer and gliomas, tumors originating in glial cells in the brain and spinal cord. Machine learning enhances cancer diagnosis and biomarker identification by detecting relevant patterns, improving prognosis. In breast cancer and gliomas, the support vector machine has proven 85.6% accurate in detecting tumor abnormalities. Challenges include limited accessibility, with only 8.33% of U.S. patients qualifying and 4.90% benefiting. Social and legal constraints exist because implementation includes linking a patient’s genetic information to a database. In conclusion, precision medicine offers promising applications but raises concerns regarding patient consent and ethics, with cancer patients focused on more in this article.
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