Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

Over 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 g...

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Autores principales:	Emily Z. Ma, Karl M. Hoegler, Albert E. Zhou
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	melanoma melanoma genomics bioinformatics machine learning deep learning Genetics QH426-470
Acceso en línea:	https://doaj.org/article/0fe6d9e136cc4baa9c6e41baf04e6eba
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spelling	oai:doaj.org-article:0fe6d9e136cc4baa9c6e41baf04e6eba2021-11-25T17:41:32ZBioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review10.3390/genes121117512073-4425https://doaj.org/article/0fe6d9e136cc4baa9c6e41baf04e6eba2021-10-01T00:00:00Zhttps://www.mdpi.com/2073-4425/12/11/1751https://doaj.org/toc/2073-4425Over 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.Emily Z. MaKarl M. HoeglerAlbert E. ZhouMDPI AGarticlemelanomamelanoma genomicsbioinformaticsmachine learningdeep learningGeneticsQH426-470ENGenes, Vol 12, Iss 1751, p 1751 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	melanoma melanoma genomics bioinformatics machine learning deep learning Genetics QH426-470
spellingShingle	melanoma melanoma genomics bioinformatics machine learning deep learning Genetics QH426-470 Emily Z. Ma Karl M. Hoegler Albert E. Zhou Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review
description	Over 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.
format	article
author	Emily Z. Ma Karl M. Hoegler Albert E. Zhou
author_facet	Emily Z. Ma Karl M. Hoegler Albert E. Zhou
author_sort	Emily Z. Ma
title	Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review
title_short	Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review
title_full	Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review
title_fullStr	Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review
title_full_unstemmed	Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review
title_sort	bioinformatic and machine learning applications in melanoma risk assessment and prognosis: a literature review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0fe6d9e136cc4baa9c6e41baf04e6eba
work_keys_str_mv	AT emilyzma bioinformaticandmachinelearningapplicationsinmelanomariskassessmentandprognosisaliteraturereview AT karlmhoegler bioinformaticandmachinelearningapplicationsinmelanomariskassessmentandprognosisaliteraturereview AT albertezhou bioinformaticandmachinelearningapplicationsinmelanomariskassessmentandprognosisaliteraturereview
_version_	1718412089163251712

Bioinformatic and Machine Learning Applications in Melanoma Risk Assessment and Prognosis: A Literature Review

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