Learning from small medical data-robust semi-supervised cancer prognosis classifier with Bayesian variational autoencoder.

ABSTRACT:

Motivation

Cancer is one of the world's leading mortality causes, and its prognosis is hard to predict due to complicated biological interactions among heterogeneous data types. Numerous challenges, such as censorship, high dimensionality and small sample size, prevent researchers from using deep learning models for precise prediction.

Results

We propose a robust Semi-supervised Cancer prognosis classifier with bAyesian variational autoeNcoder (SCAN) as a structured machine-learning framework for cancer prognosis prediction. SCAN incorporates semi-supervised learning for predicting 5-year disease-specific survival and overall survival in breast and non-small cell lung cancer (NSCLC) patients, respectively. SCAN achieved significantly better AUROC scores

SUBMITTER: Hsu TC 

PROVIDER: S-EPMC9832968 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature