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“生物医药自然语言处理前沿”研讨会

Frontier in natural langurage Processing and BioNLP



研讨会日程:

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目标

实验室研究工作的定位是以应用基础研究为主、并向基础与应用研究延伸。本实验室将以我省主要作物、畜禽及相关致病微生物的基因组、转录组、蛋白质组、代谢组和表型组为研究对象,瞄准学科前沿将各种“组学”技术与信息技术相结合,分别从基因组信息挖掘、蛋白质序列结构功能分析、代谢组分析与药物设计、表型组分析与设计育种和生物信息技术研发五个方面开展全方位的研究,深入解读各种“组学”产生的海量生物学数据,为动植物育种、种质资源保护、农业病虫害监测和防治提供新理论、新技术和新方法,为确保我省乃至周边省份的粮食安全做出贡献。

实验室依托华中农业大学建设,2013年由湖北省科技厅批准成立。位于武汉市洪山区狮子山街1号华中农业大学逸夫楼C座。占地1000平米,拥有高性能计算机群3套,总资产约1000余万元。设五个研究方向:1)基因组信息挖掘;2)蛋白质序列结构功能分析;3)代谢组分析与药物设计;4)表型组分析与设计育种;5)生物信息技术研发。

成立

2011年5月,华中农业大学为了促进生物信息学科的发展,成立了生物信息中心。经过2年多的建设,该中心初具规模,拥有了比较齐全的生物信息学研究方面的软硬件设备,并在作物基因组学和动物药物发现方面取得了比较突出的成绩。在该中心基础上,华中农业大学于2013年申报农业生物信息湖北省重点实验室,并于当年12月正式获得批准。

团队

目前本实验室已拥有一支以中青年为主体、学术思想活跃、学风优良、工作勤奋、勇于创新、结构合理的科研学术队伍。现有固定研究人员33人,管理人员1人。具有博士学位学者的30人。其中教授7人,副教授16人,讲师7人,实验师1人。核心团队入选湖北省自然科学基金创新群体。


组成

各研究单元人员如下:
1)基因组信息挖掘:教授3人、副教授6人、讲师1人
2)蛋白质序列结构功能分析:教授2人、副教授1人、讲师2人
3)代谢组分析与药物设计:教授2人、副教授3人、讲师1人
4)表型组分析与设计育种:副教授2人、讲师2人
5)生物信息技术研发:副教授5人、讲师3人

“生物医药自然语言处理前沿”研讨会

Frontier in natural langurage Processing and BioNLP

特邀报告(Keynotes)



报告人:Kevin B Cohen
科罗拉多大学丹佛分校

特邀报告一:

Everything that you need to know about language and natural language processing in 10 graphs

摘要:

Why do computational biologists and biomedical informaticists do research in natural language processing? Language processing turns out to have multiple applications in biomedical research—for example, analyzing high-throughput screens, building knowledge bases, and mining information from health records. But, language itself has specific characteristics that make it an interesting computational problem in its own right. This talk will present 10 essential facts about language, showing how they relate to the challenges of—and the opportunities for—natural language processing.

简介:

Kevin Bretonnel Cohen is the Director of the Biomedical Text Mining Group at the University of Colorado School of Medicine, and the D’Alembert Chair in Natural Language Processing for the Biomedical Domain at the Université Paris-Saclay (OR Paris-Saclay University). His book Biomedical natural language processing, written with Dina Demner-Fushman, is the standard text on the subject. Kevin’s work covers both theoretical topics, such as lexical semantics, and practical applications, such as predicting pediatric epilepsy surgery candidates.

报告人:Alex Chengyu Fang
香港城市大学

特邀报告二:

Tagging and Parsing for Bio-medical Information

摘要:

This talk is a description of corpus annotation technologies including grammatical tagging and syntactic parsing. In particular, it will aim to discuss the importance of syntactically rich information and its relevance to high-quality retrieval of biomedical information. The talk will then describe some of the recent research at the Dialogue Systems Group, City University of Hong Kong, which is aimed at the development of an event extraction system based on deep parsing in general and fine-grained verb annotation in particular.

简介:

Alex Chengyu Fang got his PhD in linguistics from University College London and held various academic positions there before joining City University of Hong Kong, where he is Associate Professor and lectures on corpus linguistics, cognitive linguistics and stylistics. He is Adjunct Professor at the Beijing University of Aeronautics and Astronautics. His research interests include syntactic parsing, term management, feature selection, event extraction, and dialogue act analysis. His major publications include English Corpora and Automated Grammatical Analysis (2007, The Commercial Press) and Text Genres and Registers: The Computation of Linguistic Features (2015, Springer). Alex is expert member of the following organisations and research labs: International Organization for Standardization, China National Technical Committee for Standardization of Terminologies and Language Resources, Hubei Provincial Research Laboratory in Bioinformatics, and Beijing Municipal Research Centre in Language Strategy and Policy.

报告人:章文
武汉大学 计算机学院

特邀报告三:

How to mine associations from biomedical data

摘要:

The development of biology, medicine and highthroughput sequencing has generated a large number of biochemical data. Mining unobserved or undiscovered associations, such as drug-disease associations and lncRNA-disease associations, is one important issue in the biomedical data mining. We studied how to mine associations from biomedical data and design the computational methods. Our methods have good performances for several hotspot problems, and demonstrate the great potential of predicting novel associations.

简介:

Wen Zhang, Ph.D., Associate professor from Computer School of Wuhan University. His research interests include recommender systems, network missing link prediction, matrix factorization, semi-supervised learning and applications of machine learning methods to biological data and problems. He published more than 40 papers on peer-reviwed journals and conferences.

报告人:Daniela Gifu
“亚历山德鲁-库扎”大学

特邀报告四:

An Overview of Semantic Relations between Entities

摘要:

Researchers in the natural language processing (NLP) field have found impressive results in deploying text mining methods to (semi)-automated tasks such as defining, discovering and extracting semantic relations between different kind of entities (e.g. medical). At the application level, semantic relations can support various NLP tasks requiring a (lightweight or heavyweight) interpretation of meaning. A key challenge is content analysis to organize, analyse and extract concepts from large amounts of data using different tools or text mining algorithms (e.g. MetaMap tool focused on biomedical text). Another challenge is text classification in order to define, for instance, entities types based on features sets (e.g. medical entities such as Disease or Drug). Moreover, many studies are based on semantic relations extracted from the biomedical data using different Question Answering (QA) systems (e.g. SemRep focused in MEDLINE citations).

简介:

Dr. Daniela GIFU is a researcher in the NLP-Group@UAIC-FII, Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi (UAIC), Romania and the Institute for Theoretical Computer Science, Romanian Academy - Iaşi branch, being, since 2013, an affiliated scientific researcher at the Center for Advanced Research in Applied Informatics, University of Craiova, Romania. Her main research interests include Natural Language Processing tasks, most of them in correlation with discourse analysis. She has (co-)authored several books and journal articles, and more than 100 conference papers, many of them focused on problems of semantic and pragmatics analysis. She was a member of the Organizing Committees of the 23 International Conferences and member of the Scientific PC of the 33th International Conferences such as ACL, JCDL, IJCAI, JCDL, LREC. She has, also, a prolific literary activity: editor in chief at the Literary Destinies magazine, Montreal, Canada, editor in chief at the Union of Professional Journalists Magazine, București, Romania, member of Romanian Writers Association of Canada, vice president of the Union of Professional Journalists of Romania.

报告人:袁晓辉
武汉理工大学

特邀报告五:

Cascade Word Embedding to Sentence Embedding: A Class Label Enhanced Approach to Phenotype Extraction

摘要:

In molecular biology, phenotypes are often described using complex semantics and diverse biomedical expressions, thereby facilitating the development of named entity recognition (NER). Here, we propose a novel approach of recognizing plant phenotypes by cascading word embedding to sentence embedding with a class label enhancement. We utilized a word embedding method to find high-frequency phenotypes with original sentences used as input in a sentence embedding method. Using this cascaded approach, we identified author-specific phenotypic expressions. In addition, we integrated a negative class label enhanced (NCLE) algorithm into our method to further optimize the training model of Sen2Vec. We used 56,748 PubMed abstracts of model organism Arabidopsis thaliana to test the effectiveness of our approach, which results in a 135% increase in the number of new phenotypic descriptions compared with the original phenotype ontology.

简介:

Xiaohui Yuan is a professor in Department of Computer Science, Wuhan University of Technology, China. He received master in Computer Science from Wuhan University of Technology in 2002 and Ph.D. degree from Hokkaido University in Applied Mathematics in 2007. He did a postdoc research at Hokkaido University from 2007-2010. He joined the Key Lab of Soybean Molecular Breeding, Chinese Academy of Science from 2010, as a leader of bioinformatics team. In 2016, Dr. Yuan moved to Wuhan University of Technology.His research concerns Artificial Intelligence and Bioinformatics. Especially text mining, reasoning and deep learning methods for integrating multi-omics data. He has published over 30 papers in scientific journals including: Nature genetics, Genome Biology, Bioinformatics, Physical Review E and Chaos.

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