Bioinformatic andMachine Learning Applications inMelanoma Risk Assessment and Prognosis: A Literature Review
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AbstractOver 100,000 people are diagnosed with cutaneous melanoma each year in the United States. Despite recent advancements in metastatic melanoma treatment, such as immunotherapy, there are still over 7000 melanoma-related deaths each year. Melanoma is a highly heterogenous disease, and many underlying genetic drivers have been identified since the introduction of next-generation sequencing. Despite clinical staging guidelines, the prognosis of metastatic melanoma is variable and difficult to predict. Bioinformatic and machine learning analyses relying on genetic, clinical, and histopathologic inputs have been increasingly used to risk stratify melanoma patients with high accuracy. This literature review summarizes the key genetic drivers of melanoma and recent applications of bioinformatic and machine learning models in the risk stratification of melanoma patients. A robustly validated risk stratification tool can potentially guide the physician management of melanoma patients and ultimately improve patient outcomes.
DescriptionThe article processing charges (APC) for this open access article were partially funded by the Health Sciences and Human Services Library's Open Access Publishing Fund for Early-Career Researchers.
Rights/TermsAttribution-NonCommercial-NoDerivatives 4.0 International
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/21058
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Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International