Corrigendum: Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.

Hao Chi Gaoge Peng Jinyan Yang Jinhao Zhang Guobin Song Xixi Xie Dorothee Franziska Strohmer Guichuan Lai Songyun Zhao Rui Wang Fang Yang Gang Tian

Front Endocrinol (Lausanne)

Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Published: May 2024

The article discusses a previously published work, focusing on updates or corrections made to its findings.
It emphasizes the importance of accurate data in the field of endocrinology and aims to clarify any misconceptions resulting from the original publication.
The corrections enhance the credibility and reliability of the research while providing readers with the most accurate information available.

[This corrects the article DOI: 10.3389/fendo.2022.1056310.].

Download full-text PDF	Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11113545	PMC
http://dx.doi.org/10.3389/fendo.2024.1421538	DOI Listing

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