ArticleJournal of inflammation research2025
Machine Learning and Mendelian Randomization Identify Six Cell Death-Related Genes Driving Ulcerative Colitis Progression and Treatment Response.
Article in Journal of inflammation research, 2025. 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The pathogenesis of ulcerative colitis (UC) is thought to involve abnormal regulation of cell death. However, key cell death-related genes (CDGs) that drive disease progression have not been fully characterized. The identification of these CDGs is thought to potentially reveal new therapeutic targets. Methods: Machine learning (ML) and Mendelian randomization (MR) methods were integrated to identify CDGs with causal effects in UC progression. The validation included immune-related analysis, drug response assessment (infliximab/vedolizumab/golimumab), patient stratification based on consensus clustering, and functional validation. Results: Six key CDG genes ( Conclusion: These six genes can be used to assess the severity of UC and predict treatment outcomes.
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.