title: Subtyping of Gliomaby Combining Gene Expression and CNVs Data Based on a Compressive Sensing Approach.
abstract: It is realized that a combined analysis of different types of genomic measurements tends to give more reliable classification 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 subtyping six types of Gliomas. Experimental results show that the proposed combined approach can 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.
authors.affiliation: Department of Biomedical Engineering, Tulane University, New Orleans, USA.
authors.forename: Wenlong
authors.initials: W
authors.lastname: Tang
authors.affiliation: Department of Biomedical Engineering, Tulane University, New Orleans, USA.
authors.forename: Hongbao
authors.initials: H
authors.lastname: Cao
authors.affiliation: Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA.
authors.forename: Ji-Gang
authors.initials: JG
authors.lastname: Zhang
authors.affiliation: Department of Biomedical Engineering, Tulane University, New Orleans, USA.
authors.forename: Junbo
authors.initials: J
authors.lastname: Duan
authors.affiliation: Department of Biomedical Engineering, Tulane University, New Orleans, USA.
authors.forename: Dongdong
authors.initials: D
authors.lastname: Lin
authors.affiliation: Department of Biomedical Engineering, Tulane University, New Orleans, USA ; Department of Biostatistics and Bioinformatics, Tulane University, New Orleans, USA.
authors.forename: Yu-Ping
authors.initials: YP
authors.lastname: Wang
keywords: CNVs data; Classification; Combined Analysis; Compressive Sensing; Gene Expression; Glioma