Skip to main content
main-content

01.12.2012 | Proceedings | Sonderheft 1/2012 Open Access

BMC Medical Informatics and Decision Making 1/2012

Semantic text mining support for lignocellulose research

Zeitschrift:
BMC Medical Informatics and Decision Making > Sonderheft 1/2012
Autoren:
Marie-Jean Meurs, Caitlin Murphy, Ingo Morgenstern, Greg Butler, Justin Powlowski, Adrian Tsang, René Witte
Wichtige Hinweise

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MJM implemented the system, carried out the ontology, provided GATE and NLP expertise, participated in curation and evaluation and drafted the manuscript. CM carried out the curation, participated in the ontology design and the system evaluation. IM carried out the curation, participated in the ontology design and the system evaluation. GB participated in the concept and approach definitions the study makes use of and participated in the fungal genomics application. JP performed the curation validation, provided expertise on fungal enzymes and their literature and reviewed the manuscript. AT conceived of the study, participated in its design and the fungal genomics application, provided overall direction of the project and reviewed the manuscript. RW participated in the concept and approach definitions the study makes use of, provided GATE and NLP expertise, contributed to the Semantic Assistants framework, and reviewed the manuscript. All authors read and approved the final manuscript.

Abstract

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.
Literatur
Über diesen Artikel

Weitere Artikel der Sonderheft 1/2012

BMC Medical Informatics and Decision Making 1/2012 Zur Ausgabe