ArticleNucleic acids research2021
CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- SELFormerMM: multimodal molecular representation learning via SELFIES, structure, text, and knowledge graph integration.Bioinformatics (Oxford, England) · 2026Article
- BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.Bioinformatics (Oxford, England) · 2026Article
- pyBiodatafuse: extending interoperability of data using modular queries across biomedical resources.Bioinformatics (Oxford, England) · 2026Article
- OmniPath: integrated knowledgebase for multi-omics analysis.Nucleic acids research · 2026Article
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives.Journal of advanced research · 2025Review
- MedKG: enabling drug discovery through a unified biomedical knowledge graph.Molecular diversity · 2025Article
- Knowledge graphs in psychiatric research: Potential applications and future perspectives.Acta psychiatrica Scandinavica · 2025Review
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- Mutual annotation-based prediction of protein domain functions with Domain2GO.Protein science : a publication of the Protein Society · 2024Article
- NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph.Bioinformatics (Oxford, England) · 2024Article
- Progress on network modeling and analysis of gut microecology: a review.Applied and environmental microbiology · 2024Review
- Natural Language Processing for Drug Discovery Knowledge Graphs: Promises and Pitfalls.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Democratizing knowledge representation with BioCypher.Nature biotechnology · 2023Article
- KG-Hub-building and exchanging biological knowledge graphs.Bioinformatics (Oxford, England) · 2023Article
- How to approach machine learning-based prediction of drug/compound-target interactions.Journal of cheminformatics · 2023Article
- ASCARIS: Positional feature annotation and protein structure-based representation of single amino acid variations.Computational and structural biotechnology journal · 2023Article
- Article
- RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine.BMC bioinformatics · 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.
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What Socratic 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 Socratic graph.