ArticleJournal of cheminformatics2025
High-throughput screening data generation, scoring and FAIRification: a case study on nanomaterials.
Article in Journal of cheminformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
In vitro-based high-throughput screening (HTS) technology is applicable to hazard-based ranking and grouping of diverse agents, including nanomaterials (NMs). We present a standardized HTS-derived human cell-based testing protocol which combines the analysis of five assays into a broad toxic mode-of-action-based hazard value, termed Tox5-score. The overall protocol includes automated data FAIRification, preprocessing and score calculation. A newly developed Python module ToxFAIRy can be used independently or within an Orange Data Mining workflow that has custom widgets for fine-tuning, included in the custom-developed Orange add-on Orange3-ToxFAIRy. The created data-handling workflow has the advantage of facilitated conversion of the FAIR HTS data into the NeXus format, capable of integrating all data and metadata into a single file and multidimensional matrix amenable to interactive visualizations and selection of data subsets. The resulting FAIR HTS data includes both raw and interpreted data (scores) in machine-readable formats distributable as data archive, including into the eNanoMapper database and Nanosafety Data Interface. We overall present a HTS-driven FAIRifed computational assessment tool for hazard analysis of multiple agents simultaneously, including with broad potential applicability across diverse scientific communities.Scientific Contribution Our study represents significant tool development for analyzing multiple materials hazards rapidly and simultaneously, aligning with regulatory recommendations and addressing industry needs. The innovative integration of in vitro-based toxicity scoring with automated data preprocessing within FAIRification workflows enhances the applicability of HTS-derived data application in the materials development community. The protocols described increase the effectiveness of materials toxicity testing and mode-of-action research by offering an alternative to manual data processing, enrichment of HTS data with metadata, refining testing methodologies-such as for bioactivity-based grouping-and overall, demonstrates the value of reusing existing data.
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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.