Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.

ABSTRACT:

Background

Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer patients. Specifically, Deep Learning versions of the Cox proportional hazards models are trained with transcriptomic data to predict survival outcomes in cancer patients.

Methods

In this study, a broad analysis was performed on TCGA cancers using a variety of Deep Learning-based models, including Cox-nnet, DeepSurv, and a method proposed by our group named AECOX (AutoEncoder with Cox regression network). Concordance index and p-value of the log-rank test are used to evaluate the model performances.

Results

All models show com

SUBMITTER: Huang Z 

PROVIDER: S-EPMC7118823 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature