Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma

BackgroundImmunotherapy has revolutionized skin cutaneous melanoma treatment, but response variability due to tumor heterogeneity necessitates robust biomarkers for predicting immunotherapy response.MethodsWe used weighted gene co-expression network analysis (WGCNA), consensus clustering, and 10 mac...

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Bibliographic Details
Main Authors: Zaidong Deng, Jie Liu, Yanxun V. Yu, Youngnam N. Jin
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-09-01
Series:Frontiers in Immunology
Subjects:
skin cutaneous melanoma
immunotherapy
tumor immune microenvironment (TIME)
machine learning
multi-omics
GBP5
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2024.1451103/full

Internet

https://www.frontiersin.org/articles/10.3389/fimmu.2024.1451103/full

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