ReviewBriefings in bioinformatics2022
Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.
Review in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Benchmarking the impact of data leakage on the performance of knowledge graph embedding models for biomedical link prediction.Bioinformatics (Oxford, England) · 2026Article
- Knowledge Graphs for drug repurposing: a review of databases and methods.Briefings in bioinformatics · 2024Review
- Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery.Journal of biomedical semantics · 2024Article
- Modeling Path Importance for Effective Alzheimer's Disease Drug Repurposing.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Developing a Knowledge Graph for Pharmacokinetic Natural Product-Drug Interactions.Journal of biomedical informatics · 2023Article
- Editorial: Emerging areas in literature-based discovery.Frontiers in research metrics and analytics · 2023Article
- Discovering causal paths to diabetic nephropathy by combining computable biomedical knowledge with graph mining algorithms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.
Indexed as
Identifiers
What OpenQuestion holds
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.