ArticleFrontiers in artificial intelligence2026
The quantified immune-aging dysregulation index: a large-language model-powered method for annotating and quantifying systems-level dysregulation.
Article in Frontiers in artificial intelligence, 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pathway enrichment analyses are widely used to interpret transcriptomic datasets; however, their outputs typically consist of lists of statistically enriched pathways that require qualitative interpretation and are difficult to compare across biological contexts. Methods of semantic classification that transform enrichment results into quantitative, mechanistically interpretable measures of system-level dysregulation remain underexplored. Methods: Here, we introduced TENSE (quanTifiEd immuNe-aging dySregulation index), a framework that summarizes pathway enrichment outputs into a quantitative estimate of immune-aging-associated dysregulation. Utilizing a Large Language Model classifier via a KNIME workflow, significantly enriched pathways are semantically classified into five mechanistic categories representing key processes implicated in immune aging, the DIRES scheme: DNA damage (D), DNA repair (R), epigenetic drift (E), inflammaging (I), and nucleic acid sensing (S). These pathway-derived signals are then aggregated into a normalized dysregulation score reflecting the magnitude (TENSE) and distribution (DIRES) of aging-associated processes across biological contexts. Results: Application of TENSE to transcriptional modules derived from neurodegenerative, radiation-response, and immune activation datasets revealed distinct dysregulation profiles. Alzheimer's disease-associated modules were primarily characterized by inflammaging signatures, particularly within microglial transcriptional programs, whereas radiation response datasets exhibited dominant DNA damage-related signals. Sepsis-associated gene signatures showed strong inflammatory contributions, producing the highest TENSE values observed. Robustness analysis demonstrated high reproducibility of pathway classification across repeated runs and close agreement between large language model-derived annotations and human consensus scores. Conclusion: TENSE provides a reproducible and interpretable method for transforming pathway enrichment outputs into quantitative estimates of system-level immune-aging dysregulation. By bridging pathway enrichment analysis and mechanistic interpretation, the framework enables comparative analysis of aging-related biological processes across diverse datasets.
Indexed as
Identifiers
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.