ArticleBriefings in bioinformatics2026
Building a credibility-based framework for target discovery: Perspectives from tRNA synthetase-linked metabolic diseases.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
While target identification is essential for successful drug discovery, no systematic workflow exists to prioritize potential targets for a given indication. Therefore, this study aims to develop an information-based approach combining text mining, network analysis, and centrality-based prioritization. As a case study, we applied this workflow to identify metabolic disease targets potentially linked to aminoacyl-tRNA synthetases (ARSs). From 1,407,654 PubMed articles, potential ARS interactors and their disease associations were mined. Using these data, the ARS interactor-disease networks were constructed based on edge frequency and citation count. To assess the reliability of these linkages, we used five centrality indices with novel visualization tools and identified 94 high-credibility disease-associated ARS interactors. Among them, two targets (ESR1 and APP) were selected for experimental validation. Although demonstrated in ARS-mediated metabolic diseases, this approach can be similarly used to identify disease-associated factors with credibility scores within any target space of interest.
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.