报告简介：
了解癌症的发展机制和挖掘潜在的相关基因或疾病相关的通路是癌症治疗的关键。随着高通量测序技术的快速发展，TCGA，ICGC等一批大型癌症基因组学项目已经形成了一个海量的高维组学数据。现有研究认为，基因或通路构成协同作用的网络影响癌症进程。因此，我们可以通过网络整合多组学数据来研究癌症发展机制。如何才能区分驱动基因或重要通路？在本次报告中，我会给出基于网络的计算方法来发现驱动基因和癌症亚型。另外，我将介绍两种基于突变数据来解决最大权重子矩阵问题的模型，以识别驱动通路。
Understanding the mechanisms of cancer development and uncovering actionable target genes or disease-related pathways is essential for cancer treatment. With rapid advances in high-throughput sequencing technologies, some large scale cancer genomics projects, such as TCGA and ICGC, have produced a sea of multi-dimensional and different omics data. And it is widely accepted that genes or pathways are often function cooperatively by interaction network in cancer progression. So we can investigate cancer progression mechanism by integrating multi-omics based on network. How do we distinguish driver genes or important pathways from passengers? In this talk, I will give the network-based computational methods to discover driver genes and cancer subtype. In addition, I will introduce two models which are designed to solve maximum weight submatrix problem based on mutation data to identify driver pathways.
3报告三：K-tuple伪核苷酸组分在DNA调控元件预测中的应用 The application of pseudo K-tuple nucleotide composition in DNA regulatory element prediction