| EPSRC Reference: |
EP/J008427/1 |
| Title: |
Language Processing for Literature Based Discovery in Medicine |
| Principal Investigator: |
Stevenson, Dr M |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Computer Science |
| Organisation: |
University of Sheffield |
| Scheme: |
Standard Research |
| Starts: |
01 June 2012 |
Ends: |
31 May 2015 |
Value (£): |
293,127
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| EPSRC Research Topic Classifications: |
| Artificial Intelligence |
Bioinformatics |
| Comput./Corpus Linguistics |
Information & Knowledge Mgmt |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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26 Oct 2011
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EPSRC ICT Responsive Mode - Oct 2011
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Announced
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06 Sep 2011
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EPSRC ICT Responsive Mode - Sep 2011
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Deferred
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Summary on Grant Application Form |
The amount of published material in biomedicine has been growing exponentially in recent years, particularly in very productive areas, such as genomics. The knowledge it contains is now so vast and fragmented that it is no longer possible for any individual or research group to keep up with the advances relevant to their area. The research literature is also fragmented and researchers naturally concentrate their attention on their own area of expertise, meaning they may not identify research that is relevant to their own if it does not appear within the literature of their scientific discipline. However, medical research is becoming increasingly interdisciplinary with progress being made by combining outputs from various fields.
Hidden knowledge occurs when a connection can be inferred by combining information from multiple documents, but that connection has not been noticed. Literature Based Discovery (LBD) provides tools that analyse the research literature to identify hidden knowledge automatically. Connections it has been used to identify include treatments for diseases (e.g. that fish oil can be used to treat Raynaud's syndrome) and cases of diseases (e.g. that migraines can be related to magnesium deficiency). Despite these successes, the knowledge that has been discovered has been limited by the relatively simple techniques used to analyse the research literature.
This project will develop new approaches to LBD by applying recent advances in the automatic processing of biomedical literature. This analysis will provide a LBD system with more detailed and accurate information about this literature than has previously been possible. In particular, the project will make use of two language processing technologies, Information Extraction and Word Sense Disambiguation, which can now be applied to the biomedical literature on a large scale. Information Extraction will be used to identify connections between items mentioned in documents and will provide more accurate analysis than the simple techniques used by previous LBD systems. Word Sense Disambiguation will be used to avoid the problems caused by polysemy and synonymy (the suggestion of spurious connections and connections being missed) which can adversely effect LBD performance.
The project will implement a LBD system and test it on two domains: oncology and neuroscience. The effectiveness of the system will be judged by researchers working in these areas with interests in melanoma and Parkinson's disease.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.shef.ac.uk |