ArticleBiology2026
Exploratory Machine Learning and Omics Integration in the Search for Biomarkers of Papillary Thyroid Cancer.
Article in Biology, 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
4 authors.
Funding
Abstract
Papillary thyroid carcinoma (PTC) is among the most common endocrine malignancies worldwide, and although generally associated with a favorable prognosis, a subset of patients develops aggressive disease with higher recurrence risk. This highlights the need for improved molecular characterization. Data integration approaches combined with computational methods offer new opportunities to refine diagnosis and uncover disease mechanisms. This study aims to integrate omics data and apply machine learning (ML) to identify clinically relevant biomarkers in papillary thyroid carcinoma. We selected 11 genes from the differentially expressed genes (DEGs)-LASSO intersection approach. Genes were validated using an independent external dataset (AUC = 91%, Sens. = 92%, Spec. = 97%, and Acc. = 95%). DEGs were integrated with metabolomics data from the literature, enabling the construction of a metabolite-gene interaction network, highlighting norepinephrine, arachidonic acid, and glutamic acid as representative metabolites, while the main genes were
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.