An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction.

ABSTRACT:

Background

Predicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types.

Methods

Here, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cance

SUBMITTER: Chai H 

PROVIDER: S-EPMC8504135 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature