ArticleiScience2025
Overcoming research bias: The untapped potential of biomedically important but understudied proteins.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Conservation of Human IgSF Proteins Throughout Eukaryotic Evolution.Genome biology and evolution · 2026Article
- Commentary: GETgene-AI: a framework for prioritizing actionable cancer drug targets.Frontiers in systems biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Researchers have concentrated much of their attention on a limited number of disease-associated proteins to develop life-changing therapeutics. However, this research bias overlooks many important therapeutic targets yet to be explored. In this work, we quantitatively assess the magnitude of this bias and describe a new approach that aggregates data from several databases to inspire the investigation of understudied but biomedically important proteins. Our method combines literature analysis, protein interaction data, and diverse sources of clinical, genetic, and molecular information to evaluate both how well a protein has been studied and its potential biomedical importance. Together, these data can be explored through either a user-friendly script or website to discover understudied, but biomedically important targets for investigation and drug discovery.
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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.