【摘 要】
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With advances in next-generation sequencing(NGS)technologies,large number of multiple types of high-throughput genomics data are available.A highly challenge for cancer genomics is to identify the dri
【出 处】
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第二届中国计算机学会生物信息学会议
论文部分内容阅读
With advances in next-generation sequencing(NGS)technologies,large number of multiple types of high-throughput genomics data are available.A highly challenge for cancer genomics is to identify the driver genes from the passenger genes by analyzing and integrating big and noisy genomics data.Breast cancer is a heterogeneous disease including five subtypes.The identification of subtype-specific biomarkers is critical to guide the diagnosis,assessment of prognosis and treatment of breast cancer.We have developed an integrated analysis frame based on gene expression profiling data and copy number aberrations data to identify breast cancer subtype-specific biomarkers.In this frame,we employed statistical machine-learning method to select genesets and utilizing an integrative method based on module network analysis to identify potential driver genes.
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