HFS-SLPEE: A Novel Hierarchical Feature Selection and Second Learning Probability Error Ensemble Model for Precision Cancer Diagnosis - Meng, Yajie; Jin, Min (Frontiers in Cell and Developmental Biology, 2021)
The emergence of high-throughput RNA-seq data has offered unprecedented opportunities for cancer diagnosis. However, capturing biological data with highly non-linear and complex associations by most existing approaches for cancer diagnosis has been challenging. In this study, we propose a novel hierarchical feature selection and second learning probability error ensemble model (named HFS-SLPEE) for precision cancer diagnosis. Specifically, We first integrate protein-coding gene expression profiles, non-coding RNA expression profiles, and DNA methylation data to provide rich information; afterwards, we design a novel hierarchical feature selection method, which takes the CpG-gene biological associations into account and can select a compact set of superior features; next, we use four individual classifiers with significant-differences and apparent-complementary to build the heterogeneous classifiers; lastly, we develop a second learning probability error ensemble model called SLPEE to thoroughly learning the new data consisting of classifiers-predicted class probability values and the actual label, further realizing the self-correction of the diagnosis errors. Benchmarking comparisons on TCGA show that HFS-SLPEE performs better than the state-of-the-art approaches. Moreover, we in-depth analyzed ten groups of selected features and found several novel HFS-SLPEE-predicted epigenomics and epigenetics biomarkers for BRCA (e.g., TSLP and ADAMTS9-AS2), LUAD (e.g., HBA1 and CTB-43E15.1), and KIRC (e.g., IRX2 and BMPR1B-AS1).