ArticleJournal of biomedical informatics2012
Discovering discovery patterns with Predication-based Semantic Indexing.
Article in Journal of biomedical informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Hyperdimensional computing in biomedical sciences: a brief review.PeerJ. Computer science · 2025Article
- Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data.PLoS computational biology · 2024Review
- Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: A use case studying depression as a risk factor for Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Validation approaches for computational drug repurposing: a review.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Review
- Ontology-based identification and prioritization of candidate drugs for epilepsy from literature.Journal of biomedical semantics · 2022Review
- Computational Literature-based Discovery for Natural Products Research: Current State and Future Prospects.Frontiers in bioinformatics · 2022Review
- Adverse Drug Event Prediction Using Noisy Literature-Derived Knowledge Graphs: Algorithm Development and Validation.JMIR medical informatics · 2021Article
- Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance.Journal of biomedical informatics · 2021Article
- Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning.Cancers · 2020Review
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance.Drug safety · 2020Article
- Rapamycin - mTOR + BRAF = ? Using relational similarity to find therapeutically relevant drug-gene relationships in unstructured text.Journal of biomedical informatics · 2019Article
- A systematic review on literature-based discovery workflow.PeerJ. Computer science · 2019Article
- Learning predictive models of drug side-effect relationships from distributed representations of literature-derived semantic predications.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.Journal of biomedical informatics · 2018Article
- Embedding of semantic predications.Journal of biomedical informatics · 2017Article
- Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Literature-Based Discovery of Confounding in Observational Clinical Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Reasoning with Vectors: A Continuous Model for Fast Robust Inference.Logic journal of the IGPL · 2015Article
- Exploring relation types for literature-based discovery.Journal of the American Medical Informatics Association : JAMIA · 2015Article
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Authors and funding
5 authors.
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Abstract
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.
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Registered trials
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