受中国矿业大学信息与控制工程学院和中国矿业大学生物信息研究所邀请，加拿大卡尔加里大学Edwin Wang教授在我校举行学术报告。欢迎广大师生踊跃参加！

报告题目：人工智能和精准医疗

时 间：11月4日下午2:00

地 点：文昌校区逸夫楼邵合3教室

主办单位：中国矿业大学信息与控制工程学院；中国矿业大学生物信息研究所

报告人简介：Edwin Wang，现任加拿大卡尔加里大学讲席教授、终身教授，曾任加拿大国家科学院高级研究员和麦吉尔大学教授。具有生物与计算双重教育背景，国际生物信息学知名专家。网络生物学和系统生物学，特别是癌症系统生物学一流学者。美国癌症研究学会（AACR）癌症系统生物学智囊团（Think Tank）的三十名领域内学术领袖之一。生物信息领域顶级期刊 PLoS Computational Biology 的编委。美国国家癌症研究所、美国国立卫生研究院，加拿大国家科学与工程研究委员会、加拿大农业部、加拿大国家创新基金会、加拿大国家卫生研究院基金项目评审专家。 主编了癌症系统生物学领域内的第一部专著（2010）。开创了microRNA/non-coding RNA基因网络研究领域。有关癌症分子网络模块的开创性研究工作被写进由诺贝尔奖获得者Hartwell博士和系统生物学之父Hood 博士主编的大学《遗传学》教科书（2014和2017年版）. 提出癌症特征分子网络计算框架，将20年来传统的癌症特征描述转化为量化网路模型，从而整合癌症组学数据，图像和电子病例，用于建模和发展假说。

报告摘要：Cancer is the leading cause of death and the third largest burden in the healthcare system in the world. Each year, more than 15 million new cancer patients are diagnosed and 7-8 million people die from cancer in the world. Current precision oncology is focusing on cancer treatment, however, with some notable exceptions, improvements in overall survival and morbidity over the past few decades have been modest. Historical data suggest that early detection of cancer is crucial for its ultimate control and prevention. To meet the challenges of the surge in cancer cases in the future, it is envisioned that, besides the promotion of lifestyle changes, improving early diagnosis is the best strategy for reducing the impact of carcinogenesis. Both genetic and environmental factors (e.g., pollution, lifestyle and so on) interact to induce cancer initiation, progression and metastasis. Therefore, we are aiming to combine the genome sequencing, imaging and electronic medical records of individuals to identify high-risk cancer individuals, ‘healthy lifestyle patterns’ for cancer prevention, and monitor high-risk cancer individuals for cancer early detection. To do so, we have complied a cohort which contains 5 million people whose medical records have been collected. Among them, 0.5 million people’ genomic information has been determined. We are developing new algorithms by applying machine learning and deep learning approaches to the cohort to meet the goals mentioned above.