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Semantic text mining support for lignocellulose research


Semantic text mining support for lignocellulose research

Meurs, Marie-Jean, Murphy, Caitlin, Morgenstern, Ingo, Butler, Greg, Powlowski, Justin, Tsang, Adrian and Witte, René (2012) Semantic text mining support for lignocellulose research. BMC Medical Informatics and Decision Making, 12 (Suppl). S5. ISSN 1472-6947

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Official URL: http://dx.doi.org/10.1186/1472-6947-12-S1-S5


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.

Divisions:Concordia University
Item Type:Article
Authors:Meurs, Marie-Jean and Murphy, Caitlin and Morgenstern, Ingo and Butler, Greg and Powlowski, Justin and Tsang, Adrian and Witte, René
Journal or Publication:BMC Medical Informatics and Decision Making
Date:30 April 2012
  • Genozymes
  • Concordia Open Access Author Fund
Digital Object Identifier (DOI):10.1186/1472-6947-12-S1-S5
Keywords:lignocellulose research, semantic computing, text mining
ID Code:973982
Deposited On:04 May 2012 15:49
Last Modified:18 Jan 2018 17:37
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