Computational Prediction of Driver Genes in Cancer Genome Sequencing Studies

未知发表者:基建办发表流年:2020-09-23浏览次数:22


主讲人:刘鹏飞渊  浙江大学教授


流年:2020年9月26日13:00


地点:三号楼332服务厅


办起单位:数理学院


主讲人介绍:刘鹏飞渊博士,2016年加盟浙江大学转化医道设计研究院以及医科院附属浙江邵逸夫医院。归国前任威斯康辛大学排名医科院的农大副教授被掐死和担任其医科院系统分子医道中心的计算边缘科学领导者。长期从事生物颗粒机延边钢结构信息学,基因家族学和癌症遗传的研究。已在生物颗粒机延边钢结构信息学等领域发表了118篇包括Nature Genetics,Nucleic Acids Research。Cancer Research。Oncogene和Bioinformatics等SCI论文网。2010年获教育部留学服务中心自发诺贝尔科学奖三等奖(第5完成人合作关系说明)。担任Physiological Genomics等杂志的编委办全称,南阳医科院兼职教授,全国继续教育网保健产业企业管理模式协会精准医疗分会剧务韩国理事,浙江省考试院生物颗粒机延边钢结构信息学学会精准医道专业委员会的英文副主委,浙江省考试院数理长春市医道会泪腺疾病常委会副主委。浙江省考试院数理长春市医道会生物颗粒机医道大数据魔方常委会中央剧务委员名单。


本末介绍:Cancer is a genetic disease with somatically acquired genomic aberrations. Driver mutations are required for the cancer phenotype, whereas passenger mutations are irrelevant to tumor development and accumulate through DNA replication. Several major cancer sequencing projects, such as The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) have created a comprehensive catalog of somatic mutations across all major cancer types. A major goal of these sequencing projects is to identify cancer genes with mutations that drive the cancer phenotype. Better identification of cancer driver genes would inform potential therapies targeted against the products of these aberrant genomic alterations in addition to fundamentally advancing the knowledge of tumor initiation, promotion and progression. In my presentation, I will briefly review several computational tools for prioritizing cancer driver genes from cancer genome sequencing projects. In particular, I will focus on two computational tools (DrGaP and DriverML) developed in my laboratory to identify cancer driving genes.

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