Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data

Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data

Abstract Background Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) dat...

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Bibliographic Details
Main Authors: Jie Hao, Youngsoon Kim, Tejaswini Mallavarapu, Jung Hun Oh, Mingon Kang
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Medical Genomics
Subjects:
Cox-PASNet
Deep neural network
Survival analysis
Glioblastoma multiforme
Ovarian cancer
Online Access:https://doi.org/10.1186/s12920-019-0624-2

Internet

https://doi.org/10.1186/s12920-019-0624-2

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