时间：1月7日（星期四）下午14:30-16:00

腾讯会议号：296 459 659

报告内容简介：

The size of biomedical literature is massive and expands at a fast rate, due to the rapid growth in biomedical research and development. PubMed is an online portal (accessing primarily the MEDLINE database) that include more than 30 million of research articles (abstracts) on life sciences and biomedical topics by the end of January 2020. Biomedical literature provides healthcare practitioners (e.g., physicians, pharmacists) up-to-date biomedical research findings, which can be applied to improve professional practices and healthcare outcomes. Moreover, biomedical literature is core to new knowledge creation and discovery. Because the size of biomedical literature expands rapidly, manual review and inspection of biomedical research articles is very difficult and time-consuming. As a result, the development of some natural language understanding (NLU) techniques that can comprehend or extract important information from this huge collection of literature is essential and desirable.

One important type of information that can be extracted from these articles are biomedical relations discussed in each article. Examples of biomedical relations include drug-disease relations, chemical-protein relations, gene-disease relations, protein interactions, drug-drug interactions, etc. Formally, given a sentence (or a small segment of text) that contains two entities of interest, the task of  relation extraction is to predict whether the sentence describes some relation (out of a predefined set of relation types) between the two entities and, if so, to classify which relation class does the sentence point to. In this talk, I will present our proposed biomedical relation extraction methods that follow the deep-learning-based approach. In addition, in this talk, I will also discuss an important application of biomedical relation extraction, i.e., literature-based drug repurposing.

报告人简介：

魏志平教授目前任职于台湾大学信息管理系，担任特聘教授。魏教授为美国亚历桑那大学管理信息系统博士(1996年毕业)，曾于清华大学以及中山大学任教，亦曾于美国华盛顿大学、美国伊利诺大学香槟分校、香港中文大学担任访问学者。

魏教授主要研究领域为大数据分析、文字探勘、社群媒体分析、生医信息、专利分析与探勘等，其研究成果发表于信息管理或信息科技相关领域之国际知名期刊中，例如 Journal of Management Information Systems (JMIS) 、 European Journal of Information Systems (EJIS) 、 Decision Sciences 、 Decision Support Systems (DSS) 、 Information & Management (I&M) 、 Journal of the Association for Information Science and Technology 、 IEEE Transactions in Engineering Management ,  IEEE Transactions on Systems, Man, and Cybernetics 、 IEEE Intelligent Systems 、 IEEE Transactions on Information Technology in Biomedicine 等。

（承办：管理工程系、科研与学术交流中心）