Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder - Shen, Junjie; Li, Huijun; Yu, Xinghao; Bai, Lu; Dong, Yongfei; Cao, Jianping; Lu, Ke; Tang, Zaixiang (Frontiers in Oncology, 2023)
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 prediction model for improving prediction power. We find that 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 trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models, and evaluated raw predictive effect of the models. The results indicated that these compressed transformation features could better improve model’s original predictive performance and might avoid over-fitting problem. This idea may provide something enlightened for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.