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Semantic Software Lab
Concordia University
Montréal, Canada

Text Mining

Open Mutation Miner (OMM)

Mutations as sources of evolution have long been the focus of attention in the biomedical literature. Accessing the mutational information and their impacts on protein properties facilitates research in various domains, such as enzymology and pharmacology. However, manually reading through the rich and fast growing repository of biomedical literature is expensive and time-consuming. Text mining methods can help by automatically analysing the literature and extracting mutation-related knowledge into a structured represenation.

Our Open Mutation Miner (OMM) system provides a number of advanced text mining components for mutation mining from full-text research papers, including the detection of various forms of mutation mentions, protein properties, organisms, impact mentions, and the relations between them. OMM provides output options in various formats, including populating an OWL ontology, Web service access, structured queries, and interactive use embedded in desktop clients. It is described and evaluated in detail in our paper, Naderi, N., and R. Witte, "Automated extraction and semantic analysis of mutation impacts from the biomedical literature", BMC Genomics, vol. 13, no. Suppl 4, pp. S10, 06/2012.

OwlExporter v3.0 Released


We just released a new version of the OwlExporter ontology population plugin for GATE. The OwlExporter PR can be added to any NLP pipeline to facilitate the population of an existing OWL ontology with entities detected in the corpus. It supports the population of separate NLP- and domain-ontologies and has support for some advanced features, like the export of coreference chains.

In this release, we included a pre-compiled binary and a complete example pipeline that transforms GATE's ANNIE information extraction example into an ontology population system. We also completely revamped the documentation and website to make it more accessible to ontology population novices.

The OrganismTagger System


Our open source OrganismTagger is a hybrid rule-based/machine-learning system that extracts organism mentions from the biomedical literature, normalizes them to their scientific name, and provides grounding to the NCBI Taxonomy database. Our pipeline provides the flexibility of annotating the species of particular interest to bio-engineers on different corpora, by optionally including detection of common names, acronyms, and strains. The OrganismTagger performance has been evaluated on two manually annotated corpora, OT and Linneaus. On the OT corpus, the OrganismTagger achieves a precision and recall of 95% and 94% and a grounding accuracy of 97.5%. On the manually annotated corpus of Linneaus-100, the results show a precision and recall of 99% and 97% and grounding with an accuracy of 97.4%. It is described in detail in our publication, Naderi, N., T. Kappler, C. J. O. Baker, and R. Witte, "OrganismTagger: Detection, normalization, and grounding of organism entities in biomedical documents", Bioinformatics, vol. 27, no. 19 Oxford University Press, pp. 2721--2729, August 9, 2011.

Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten

Witte, R., and J. Mülle (Eds.), Text Mining: Wissensgewinnung aus natürlichsprachigen Dokumenten, Universität Karlsruhe, Fakultät für Informatik, Institut für Programmstrukturen und Datenorganisation (IPD), 2006.

Mutation Miner

Baker, C. J. O., R. Witte, A. B. Gurpur, and V. Ryzhikov, "Mutation Miner", 5th International Conference of the Canadian Proteomics Initiative (CPI 2005), Toronto, Ontario, Canada, May 13–14, 2005.

Mutation Miner

Baker, C. J. O., and R. Witte, "Mutation Miner", 13th Annual International conference on Intelligent Systems for Molecular Biology (ISMB 2005), Detroit, Michigan, USA, June 25–29, 2005.

Ontology Design for Biomedical Text Mining

Baker, C. J. O., and K. - H. Cheung (Eds.), Witte, R., T. Kappler, and C. J. O. Baker, "Ontology Design for Biomedical Text Mining", Semantic Web: Revolutionizing Knowledge Discovery in the Life Sciences, New York, NY, USA : Springer Science+Business Media, pp. 281–313, 2007.
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