ArticleAnnual review of biomedical data science2020
Knowledge-Based Biomedical Data Science.
Article in Annual review of biomedical data science, 2020. 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.
- A Perspective on Software Intelligence for Autonomous Transformations in Biomedical Data and Knowledge.Learning health systems · 2026Article
- Unlocking biological insights from differentially expressed genes: Concepts, methods, and future perspectives.Journal of advanced research · 2025Review
- Creating an Interactive Web Interface for Networks Stored in Knowledge Graph Databases.Current protocols · 2025Article
- MedKG: enabling drug discovery through a unified biomedical knowledge graph.Molecular diversity · 2025Article
- medicX-KG: a knowledge graph for pharmacists' drug information needs.Journal of biomedical semantics · 2025Article
- Knowledge graphs in psychiatric research: Potential applications and future perspectives.Acta psychiatrica Scandinavica · 2025Review
- Hybrid deep learning models for text-based identification of gene-disease associations.BioImpacts : BI · 2025Article
- An open source knowledge graph ecosystem for the life sciences.Scientific data · 2024Article
- Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base.BMC bioinformatics · 2024Article
- In Silico Clinical Trials: Is It Possible?Methods in molecular biology (Clifton, N.J.) · 2024Article
- An exploration of knowledge-organizing technologies to advance transdisciplinary back pain research.JOR spine · 2023Article
- A scoping review on multimodal deep learning in biomedical images and texts.Journal of biomedical informatics · 2023Article
- Democratizing knowledge representation with BioCypher.Nature biotechnology · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.Research square · 2023Article
- Creating an ignorance-base: Exploring known unknowns in the scientific literature.Journal of biomedical informatics · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.medRxiv : the preprint server for health sciences · 2023Article
- From language models to large-scale food and biomedical knowledge graphs.Scientific reports · 2023Article
- Article
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Knowledge-based biomedical data science involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey recent progress in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as progress on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing to construct knowledge graphs, and the expansion of novel knowledge-based approaches to clinical and biological domains.
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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.