Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology...

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Bibliographic Details
Main Authors: Satoshi Takahashi, Ken Asada, Ken Takasawa, Ryo Shimoyama, Akira Sakai, Amina Bolatkan, Norio Shinkai, Kazuma Kobayashi, Masaaki Komatsu, Syuzo Kaneko, Jun Sese, Ryuji Hamamoto
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Biomolecules
Subjects:
lung cancer
multi-omics analysis
deep learning and machine learning
Online Access:https://www.mdpi.com/2218-273X/10/10/1460

Internet

https://www.mdpi.com/2218-273X/10/10/1460

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