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Subtyping of Gliomaby Combining Gene Expression and CNVs Data Based on a Compressive Sensing Approach

Wenlong Tang, Hongbao Cao, Ji-Gang Zhang, Junbo Duan, Dongdong Lin and Yu-Ping Wang

It is realized that a combined analysis of different types of genomic measurements tends to give more
reliableclassification results. However, how to efficiently combine data with different resolutions is challenging. We propose a novel compressed sensing based approach for the combined analysis of gene expression and copy number variants data for the purpose of subtypingsix types of Gliomas.Experimental results show that the proposed combined approachcan substantially improve the classification accuracy compared to that of using either of individual data type. The proposed approach can be applicable to many other types of genomic data.