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Discovering discovery patterns with predication-based Semantic Indexing
- Source :
- Journal of Biomedical Informatics. 45:1049-1065
- Publication Year :
- 2012
- Publisher :
- Elsevier BV, 2012.
-
Abstract
- Graphical abstractDisplay Omitted Highlights? PSI represents concepts and relations in hyperdimensional space. ? PSI is used to infer discovery patterns from known therapeutic relationships. ? These patterns are used to recover therapeutic relationships for a held-out disease set. ? PSI outperforms a co-occurrence based approach in this regard. ? PSI searches efficiently across large networks of relevant relationships.. In this paper we utilize methods of hyperdimensional computing to mediate the identification of therapeutically useful connections for the purpose of literature-based discovery. Our approach, named Predication-based Semantic Indexing, is utilized to identify empirically sequences of relationships known as "discovery patterns", such as "drug x INHIBITS substance y, substance y CAUSES disease z" that link pharmaceutical substances to diseases they are known to treat. These sequences are derived from semantic predications extracted from the biomedical literature by the SemRep system, and subsequently utilized to direct the search for known treatments for a held out set of diseases. Rapid and efficient inference is accomplished through the application of geometric operators in PSI space, allowing for both the derivation of discovery patterns from a large set of known TREATS relationships, and the application of these discovered patterns to constrain search for therapeutic relationships at scale. Our results include the rediscovery of discovery patterns that have been constructed manually by other authors in previous research, as well as the discovery of a set of previously unrecognized patterns. The application of these patterns to direct search through PSI space results in better recovery of therapeutic relationships than is accomplished with models based on distributional statistics alone. These results demonstrate the utility of efficient approximate inference in geometric space as a means to identify therapeutic relationships, suggesting a role of these methods in drug repurposing efforts. In addition, the results provide strong support for the utility of the discovery pattern approach pioneered by Hristovski and his colleagues.
- Subjects :
- Predication-based Semantic Indexing
Abstracting and Indexing
Computer science
MEDLINE
Literature-based discovery
Inference
Health Informatics
Space (commercial competition)
Machine learning
computer.software_genre
Article
Pattern Recognition, Automated
Set (abstract data type)
03 medical and health sciences
0302 clinical medicine
Drug Therapy
Drug Discovery
030212 general & internal medicine
Natural Language Processing
030304 developmental biology
Distributional semantics
0303 health sciences
Information retrieval
business.industry
Publications
Search engine indexing
Vector symbolic architectures
Semantics
Computer Science Applications
Identification (information)
Approximate inference
Pharmaceutical Preparations
Artificial intelligence
business
computer
Algorithms
Subjects
Details
- ISSN :
- 15320464
- Volume :
- 45
- Database :
- OpenAIRE
- Journal :
- Journal of Biomedical Informatics
- Accession number :
- edsair.doi.dedup.....c6effe681f0f7576324a440f434e2a29