Network-based multi-task learning models for biomarker selection and cancer outcome prediction
Published in Bioinformatics, 2019
Detecting cancer gene expression and transcriptome changes with mRNA-sequencing or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene–sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types.
Recommended citation: Zhibo Wang, Zhezhi He, Milan Shah, Teng Zhang, Deliang Fan, Wei Zhang, Network-based multi-task learning models for biomarker selection and cancer outcome prediction, Bioinformatics, Volume 36, Issue 6, March 2020, Pages 1814–1822, https://doi.org/10.1093/bioinformatics/btz809
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