ArticleScientific reports2026
DataXflowGen for GenAI-driven model generation.
Article in Scientific reports, 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
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For years, mathematical models have been successfully used to explain biological, chemical, or physical relationships. The enormous advances in artificial intelligence in understanding context-specific content can support humans in generating interaction hypotheses for modeling, enabling models to be generated and modified far more quickly. Our pipeline, DataXflowGen, uses GenAI to create models based on information such as research publications available on the internet. This approach reduces the need for specialized knowledge, as GenAI identifies relevant research papers online and extracts the relevant information. GenAI can build a model that fits the data to test its hypothesis. As a result, humans only need to conduct an in-depth analysis of those model components that do not align with the data, saving time when building a suitable regulatory network based on existing knowledge. In summary, GenAI is used to generate a human-interpretable model as a hypothesis, allowing an understanding of therapy suggestions and an explanation of actions based on a model validated with specific data, avoiding black-box AI decisions. This constitutes an approach to explainable AI that supports humans in analyzing complex relationships.
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What Socratic 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 Socratic graph.