Semantic text mining support for lignocellulose research

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• 作者：Marie-Jean Meurs (1) (2)
  Caitlin Murphy (2) (3)
  Ingo Morgenstern (2) (3)
  Greg Butler (1) (2)
  Justin Powlowski (2) (4)
  Adrian Tsang (2) (3)
  René Witte (1)
  
• 刊名：BMC Medical Informatics and Decision Making
• 出版年：2012
• 出版时间：December 2012
• 年：2012
• 卷：12
• 期：1-supp
• 全文大小：1270KB
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• 作者单位：Marie-Jean Meurs (1) (2)
  Caitlin Murphy (2) (3)
  Ingo Morgenstern (2) (3)
  Greg Butler (1) (2)
  Justin Powlowski (2) (4)
  Adrian Tsang (2) (3)
  René Witte (1)
  
  1. Department of Computer Science and Software Engineering, Concordia University, Montréal, QC, Canada
  2. Centre for Structural and Functional Genomics, Concordia University, Montréal, QC, Canada
  3. Department of Biology, Concordia University, Montreal, QC, Canada
  4. Department of Chemistry and Biochemistry, Concordia University, Montréal, QC, Canada
  

文摘

Background 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. Results 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. Conclusions 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.