Finding disagreement pathway signatures and constructing an ensemble model for cancer classification.

ABSTRACT: Cancer classification based on molecular level is a relatively routine research procedure with advances in high-throughput molecular profiling techniques. However, the number of genes typically far exceeds the number of the sample size in gene expression studies. The existing gene selection methods are almost based on statistics and machine learning, overlooking relevant biological principles or knowledge while working with biological data. Here, we propose a robust ensemble learning paradigm, which incorporates multiple pathways information, to predict cancer classification. We compare the proposed method with other methods, such as Elastic SCAD and PPDMF, and estimate the classification performance. The results show that the proposed method has the higher performances on most metrics and

SUBMITTER: Zhang Q 

PROVIDER: S-EPMC5577098 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature