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Abstract:
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When large amount of quantitative information of gene expression, DNA methylation, DNA copy numbers, mutation status, etc. becomes available via advanced technology such as microarray and sequencing for a specific cell line, how to integrate all types of data to infer a disease-prone genetic information becomes a challenging task in modern statistical research. Numerous work has been published regarding how to make use of one type of the genomic data sources for studying a particular biological inference. In this paper, we propose an integrative analysis of genomics data, prognostics and survival data under a framework of an accelerated failure time with frailty (AFTF) model to infer patient survival. The proposed integrative approach aims to answer some aspects of the complex problem in genomic data analysis. We conducted extensive simulation studies to assess the performance of the proposed method with two types of genomic data, DNA methylation data and copy number variation data, on 600 genes and three clinical covariates. The simulation results show promises of the proposed method. The method will be applied to TCGA cancer data for validation.
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