Evidence mapPaperPMID 42589641Full record

ArticleInternational journal of molecular sciences2026

A Generalizable and Interpretable Framework for Molecular Subtype Classification of Pancreatic Ductal Adenocarcinoma Integrating Conformal Uncertainty Quantification and Consensus-Based Explainable Artificial Intelligence Across Multiple Cohorts.

Seyma Yasar, Fatma Hilal Yagin, Sarah A Alzakari, Amal K Alkhalifa, Fahaid Al-Hashem, Abedelmalek Kalefh Tabnjh

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Seyma YasarDepartment of Biostatistics and Medical Informatics, Faculty of Medicine, İnönü University, Malatya 44280, Türkiye.ORCID 0000-0003-1300-3393
Fatma Hilal YaginDepartment of Biostatistics, Faculty of Medicine, Malatya Turgut Ozal University, Malatya 44210, Türkiye.ORCID 0000-0002-9848-7958
Sarah A AlzakariDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Amal K AlkhalifaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0002-7273-4041
Fahaid Al-HashemDepartment of Physiology, College of Medicine, King Khalid University, Abha 61421, Saudi Arabia.ORCID 0000-0001-5795-9966
Abedelmalek Kalefh TabnjhDepartment of Cariology, Institute of Odontology, The Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden.ORCID 0000-0001-8263-4944

Funding

Princess Nourah bint Abdulrahman University PNURSP2026R716
6 · The paper itself

Abstract

Pancreatic ductal adenocarcinoma (PDAC) has two principal molecular subtypes-classical and basal-like-with divergent prognosis and chemotherapy response, yet transcriptomic classifiers rarely generalize across cohorts or quantify per-patient uncertainty. We trained a classical-versus-basal-like classifier on CPTAC-PDAC (

Indexed as

Artificial IntelligenceCarcinoma, Pancreatic DuctalPancreatic NeoplasmsAlgorithmsBiomarkers, TumorClassification AlgorithmsGene Expression ProfilingGene Expression Regulation, NeoplasticHumansUncertaintyBiomarkers, Tumorconformal predictionCPTACcross-cohort validationexplainable AImolecular subtypepancreatic ductal adenocarcinomaTCGATRIPOD+AI

Identifiers

PMID42589641
PMCPMC13467211

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.