ArticleBioinformatics advances2025
Biological databases in the age of generative artificial intelligence.
Article in Bioinformatics advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- React-to-Me: A Conversational Interface for Interactive Exploration of the Reactome Pathway Knowledgebase.Research square · 2026Article
- Architectural good practices for reproducible benchmarking in protein machine learning.Frontiers in bioinformatics · 2026Article
- PDMD: A Comprehensive Repository of Plants Reported for Skeletal Muscle-related Ailments.Current drug targets · 2026Article
- Empowering biological knowledgebases: advances in human-in-the-loop AI-driven literature curation.Bioinformatics advances · 2026Review
- Desiderata for a biomedical knowledge network: opportunities, challenges and future directions.Bioinformatics advances · 2026Article
- React-to-Me: A Conversational Interface for Interactive Exploration of the Reactome Pathway Knowledgebase.bioRxiv : the preprint server for biology · 2025Article
- Desiderata for a biomedical knowledge network: opportunities, challenges and future Directions.ArXiv · 2025Article
- Article
- Integrating Artificial Intelligence in Next-Generation Sequencing: Advances, Challenges, and Future Directions.Current issues in molecular biology · 2025Review
- Multimodal knowledge expansion widget powered by plant protein phosphorylation database and ChatGPT.Frontiers in bioinformatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: Modern biological research critically depends on public databases. The introduction and propagation of errors within and across databases can lead to wasted resources as scientists are led astray by bad data or have to conduct expensive validation experiments. The emergence of generative artificial intelligence systems threatens to compound this problem owing to the ease with which massive volumes of synthetic data can be generated. We provide an overview of several key issues that occur within the biological data ecosystem and make several recommendations aimed at reducing data errors and their propagation. We specifically highlight the critical importance of improved educational programs aimed at biologists and life scientists that emphasize best practices in data engineering. We also argue for increased theoretical and empirical research on data provenance, error propagation, and on understanding the impact of errors on analytic pipelines. Furthermore, we recommend enhanced funding for the stewardship and maintenance of public biological databases. Availability and implementation: Not applicable.
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.