ArticleJournal of biomedical informatics2017
Embedding of semantic predications.
Article in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches.Frontiers in pharmacology · 2019Pooled it
- Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.Chemical research in toxicology · 2026Article
- 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
- Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study.JMIR medical informatics · 2022Article
- 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
- Drug repurposing for COVID-19 via knowledge graph completion.Journal of biomedical informatics · 2021Article
- Broad-coverage biomedical relation extraction with SemRep.BMC bioinformatics · 2020Article
- Exploring Novel Computable Knowledge in Structured Drug Product Labels.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 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
- Predicting Adverse Drug-Drug Interactions with Neural Embedding of Semantic PredicationsAMIA ... Annual Symposium proceedings. AMIA Symposium · 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
- Predicting drug-disease associations by using similarity constrained matrix factorization.BMC bioinformatics · 2018Article
- Rediscovering Don Swanson: the Past, Present and Future of Literature-Based Discovery.Journal of data and information science (Warsaw, Poland) · 2017Article
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2 authors.
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Abstract
This paper concerns the generation of distributed vector representations of biomedical concepts from structured knowledge, in the form of subject-relation-object triplets known as semantic predications. Specifically, we evaluate the extent to which a representational approach we have developed for this purpose previously, known as Predication-based Semantic Indexing (PSI), might benefit from insights gleaned from neural-probabilistic language models, which have enjoyed a surge in popularity in recent years as a means to generate distributed vector representations of terms from free text. To do so, we develop a novel neural-probabilistic approach to encoding predications, called Embedding of Semantic Predications (ESP), by adapting aspects of the Skipgram with Negative Sampling (SGNS) algorithm to this purpose. We compare ESP and PSI across a number of tasks including recovery of encoded information, estimation of semantic similarity and relatedness, and identification of potentially therapeutic and harmful relationships using both analogical retrieval and supervised learning. We find advantages for ESP in some, but not all of these tasks, revealing the contexts in which the additional computational work of neural-probabilistic modeling is justified.
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