Semantic text mining support for lignocellulose research.
Semantic text mining support for lignocellulose research.
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DOI：
10.1186/1472-6947-12-s1-s5
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发表时间：
2012-04-30
影响因子：
3.5
通讯作者：
Witte R
中科院分区：
文献类型：
--
作者：
Meurs MJ;Murphy C;Morgenstern I;Butler G;Powlowski J;Tsang A;Witte R
从生物质生产的生物燃料被认为是化石燃料的有前途的可持续替代品。将木质纤维素转化为用于生物燃料生产的可发酵糖需要使用能够有效且经济地水解木质纤维素生物质的酶混合物。由于许多真菌天然地分解木质纤维素，所涉及的酶的鉴定和表征是生物质衍生产品和燃料的研究和开发中的关键挑战。应对这一挑战的一种方法是从快速扩展的微生物基因组库中挖掘具有适当催化特性的酶。语义技术，包括自然语言处理、本体论、语义万维网服务和基于万维网的协作工具，有望支持用户处理复杂数据，从而促进知识密集型任务。目前的一项挑战是选择适当的技术，并将它们联合收割机纳入一个连贯的系统，为用户带来可衡量的改进。我们目前正在开发的语义基础设施，以支持基于基因组学的木质纤维素研究。这项工作的一部分是从文献和基因组资源中获得的关于真菌酶的信息中自动整理知识。与手动管理现有文献的真菌生物学研究人员密切合作，我们开发了集成在基于Web的界面中的本体论自然语言处理管道，以帮助他们完成两项主要任务：挖掘文献中的相关知识，同时提供丰富的语义链接信息。
Biofuels produced from biomass are considered to be promising sustainable alternatives to fossil fuels. The conversion of lignocellulose into fermentable sugars for biofuels production requires the use of enzyme cocktails that can efficiently and economically hydrolyze lignocellulosic biomass. As many fungi naturally break down lignocellulose, the identification and characterization of the enzymes involved is a key challenge in the research and development of biomass-derived products and fuels. One approach to meeting this challenge is to mine the rapidly-expanding repertoire of microbial genomes for enzymes with the appropriate catalytic properties. Semantic technologies, including natural language processing, ontologies, semantic Web services and Web-based collaboration tools, promise to support users in handling complex data, thereby facilitating knowledge-intensive tasks. An ongoing challenge is to select the appropriate technologies and combine them in a coherent system that brings measurable improvements to the users. We present our ongoing development of a semantic infrastructure in support of genomics-based lignocellulose research. Part of this effort is the automated curation of knowledge from information on fungal enzymes that is available in the literature and genome resources. Working closely with fungal biology researchers who manually curate the existing literature, we developed ontological natural language processing pipelines integrated in a Web-based interface to assist them in two main tasks: mining the literature for relevant knowledge, and at the same time providing rich and semantically linked information.