Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder.

ABSTRACT: Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder w

SUBMITTER: Shen J 

PROVIDER: S-EPMC9872139 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature