Details of Group leader - Dr Goran Nenadic
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Post: Lecturer, School of Computer Science Phone: (0)161 306 5936 Email: g.nenadic@manchester.ac.uk Website: Click Me |
Research Text mining and automatic knowledge extraction in the domain of life sciences |
Current Research
Mining term associations from literature to support knowledge discovery in biology
The aim of this BBSRC-funded project is to develop text mining services that can provide efficient and sophisticated knowledge acquisition, offer plausible hypotheses for testing, prevent unnecessary repetition of previous work, and help in experimental design for specific research scenarios. We investigate various text mining approaches to establishing literature-based associations and links among various biological entities such as proteins, genes, species, cells, and experiments. The project explores suitable technologies (including kernel-based techniques) for modelling user-elicited biological text mining scenarios, and builds on work done in a previous BBSRC project (“Protein Functional Classification using Text Data-mining”) that has developed automatic text-based classification of proteins to functional categories (based on the Gene ontology) using machine learning techniques and various textual features.
Mining bioinformatics services, resources and workflows from documents
There are a number of services and resources available to the bioinformatics community, but meta-data that describe them is typically scarce. This project aims to develop text mining techniques to automatically describe, locate, retrieve and reason about bioinformatics services and resources. We investigate methods that extract descriptions from various document types (articles, reviews, application notes, email archives, discussion forums, etc), and map them to service descriptions using both general service ontologies and domain-specific ontologies. As a working and target environment, the project uses the myGRID/Taverna infrastructure.
Machine learning approach to sentiment analysis
Sentiment analysis is the extraction of attitudes and opinions from human-authored documents. The capture and analysis of such attitudes and opinions in an automated and structured fashion might offer a powerful technology to a number of problem domains, including business intelligence, marketing, national security, and crime prevention. This project aims to develop technologies for extraction and analysis of sentiment from free text using a combination of natural language processing (NLP), text mining and machine learning techniques. The work will evolve building models of sentiment from which suitable templates for extraction will be designed. Apart from the domains mentioned above, the approach will be tested in the scientific domain (testing the hypothesis that scientific articles involve less sentiment than other genres).
Service & Awards
- Daiwa-Adrian Foundation award (team leaders Prof Tsujii and Dr Ananiadou), 2005
- Invited speaker, Workshop on Natural Language Processing and Ontology Building in Biology, Tokyo, 2002
- Reviewer for Bioinformatics; BMC Bioinformatics; ACM Transactions on ALIP; Review of the National Centre for Digitisation;
- Program committee member of ISMB (text mining track, 2004 - ), ECCB (text mining track, 2005 - ), SMBM 2006, etc.
- Visiting lecturer in text and data mining, Department of Computer science, Faculty of Mathematics, University of Belgrade (2006 - )
Funding
BBSRC - Daiwa-Adrian Foundation - EPSRC - QinetiQ
Recent Publications
- Nenadic, G., Ananiadou, S., Mining Semantically Related Terms from Biomedical Literature, ACM Transactions on ALIP, Text Mining and Management in Biomedicine, 5, 1-22, .
- Rebholz-Schuhmann, D., Kirsch, H., Gaudan, S., Arregui, M., Nenadic, G., Annotation and Disambiguation of Semantic Types in Biomedical Text: a Cascaded Approach to Named Entity Recognition, EACL: Workshop on XML and NLP, , , 2006.
- Rice, S., Nenadic, G., Stapley, B., Mining Protein Function from Text Using Term-based Support Vector Machines, BMC Bioinformatics, 6(Suppl. 1), , 2005.
- Krauthammer, M., Nenadic, G., Term Identification in the Biomedical Literature, Journal of Biomedical Informatics, 37, 512-526, 2004.
- • Nenadic, G., Spasic, I., Ananiadou, S., Terminology-driven Mining of Biomedical Literature, Bioinformatics, 19, 938-943, 2003.
- Nenadic, G., Mima, H., Spasic, I., Ananiadou, S., Tsujii, J., Terminology-based Literature Mining and Knowledge Acquisition in Biomedicine, International Journal of Medical Informatics, 67, 33-48, 2002.