Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Artificial Intelligence - On connectionist symbol processing
An interactive system for finding complementary literatures: a stimulus to scientific discovery
Artificial Intelligence - Special issue on scientific discovery
Using latent semantic indexing for literature based discovery
Journal of the American Society for Information Science
Sparse Distributed Memory
Binary Spatter-Coding of Ordered K-Tuples
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
Holographic Reduced Representation: Distributed Representation for Cognitive Structures
LitLinker: capturing connections across the biomedical literature
Proceedings of the 2nd international conference on Knowledge capture
Journal of Biomedical Informatics - Special issue: Unified medical language system
Introduction to Information Retrieval
Introduction to Information Retrieval
Methodological Review: Empirical distributional semantics: Methods and biomedical applications
Journal of Biomedical Informatics
Journal of Biomedical Informatics
Arguments of nominals in semantic interpretation of biomedical text
BioNLP '10 Proceedings of the 2010 Workshop on Biomedical Natural Language Processing
The Semantic Vectors Package: New Algorithms and Public Tools for Distributional Semantics
ICSC '10 Proceedings of the 2010 IEEE Fourth International Conference on Semantic Computing
Finding schizophrenia's Prozac: emergent relational similarity in predication space
QI'11 Proceedings of the 5th international conference on Quantum interaction
DS'05 Proceedings of the 8th international conference on Discovery Science
Combining semantic relations and DNA microarray data for novel hypotheses generation
ISMB/ECCB'09 Proceedings of the 2009 workshop of the BioLink Special Interest Group, international conference on Linking Literature, Information, and Knowledge for Biology
Real, complex, and binary semantic vectors
QI'12 Proceedings of the 6th international conference on Quantum Interaction
Many paths lead to discovery: analogical retrieval of cancer therapies
QI'12 Proceedings of the 6th international conference on Quantum Interaction
Many paths lead to discovery: analogical retrieval of cancer therapies
QI'12 Proceedings of the 6th international conference on Quantum Interaction
A methodology for extending domain coverage in SemRep
Journal of Biomedical Informatics
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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.