2019/05/23 by Yunchuan Kong, Kong, Yunchuan, Tianwei Yu +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #Advanced Graph Neural Networks #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1905.09889
openalex publication_date 2019/05/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A unique challenge in predictive model building for omics data has been the small number of samples (n) versus the large amount of features (p). This "n≪ p" property brings difficulties for disease outcome classification using deep learning techniques. Sparse learning by incorporating external gene network information such as the graph-embedded deep feedforward network (GEDFN) model has been a solution to this issue. However, such methods require an existing feature graph, and potential mis-specification of the feature graph can be harmful on classification and feature selection. To address this limitation and develop a robust classification model without relying on external knowledge, we propose a \underlineforest \underlinegraph-\underlineembedded deep feedforward \underlinenetwork (forgeNet) model, to integrate the GEDFN architecture with a forest feature graph extractor, so that the feature graph can be learned in a supervised manner and specifically constructed for a given prediction task. To validate the method's capability, we experimented the forgeNet model with both synthetic and real datasets. The resulting high classification accuracy suggests that the method is a valuable addition to sparse deep learning models for omics data.