ArticleCPT: pharmacometrics & systems pharmacology2014
Predicting high-throughput screening results with scalable literature-based discovery methods.
Article in CPT: pharmacometrics & systems pharmacology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data.PLoS computational biology · 2024Review
- Computational methods directed towards drug repurposing for COVID-19: advantages and limitations.RSC advances · 2021Review
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- 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
- Using Literature Based Discovery to Gain Insights Into the Metabolomic Processes of Cardiac Arrest.Frontiers in research metrics and analytics · 2021Article
- 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
- Non-Negative Matrix Factorization for Drug Repositioning: Experiments with the repoDB Dataset.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations.Journal of biomedical informatics · 2018Article
- MD-Miner: a network-based approach for personalized drug repositioning.BMC systems biology · 2017Article
- Embedding of semantic predications.Journal of biomedical informatics · 2017Article
- Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases.Gastroenterology · 2017Review
- Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
- Biotechnology landscape in cancer drug discovery.Future science OA · 2015Article
- Reasoning with Vectors: A Continuous Model for Fast Robust Inference.Logic journal of the IGPL · 2015Article
- Networks of neuroinjury semantic predications to identify biomarkers for mild traumatic brain injury.Journal of biomedical semantics · 2015Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The identification of new therapeutic uses for existing agents has been proposed as a means to mitigate the escalating cost of drug development. A common approach to such repurposing involves screening libraries of agents for activities against cell lines. In silico methods using knowledge from the biomedical literature have been proposed to constrain the costs of screening by identifying agents that are likely to be effective a priori. However, results obtained with these methods are seldom evaluated empirically. Conversely, screening experiments have been criticized for their inability to reveal the biological basis of their results. In this paper, we evaluate the ability of a scalable literature-based approach, discovery-by-analogy, to identify a small number of active agents within a large library screened for activity against prostate cancer cells. The methods used permit retrieval of the knowledge used to infer their predictions, providing a plausible biological basis for predicted activity.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.