An Exploratory Transcriptomic Classification Model for Psoriasis Based on Apoptosis-Associated and Proliferation-Apoptosis-Coupled Genes Using Explainable Machine Learning.

Xinhao Liu, Wenqing Fu, Jiachen Li, Mengyang Jing, Xuli Zhu, Wenhao Bo

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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.

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6 authors.

Xinhao LiuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.

Wenqing FuState Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing 100083, China.

Jiachen LiCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0009-0007-9769-8503

Mengyang JingCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.

Xuli ZhuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0002-7925-1069

Wenhao BoCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0003-4615-4366

Funding

Fundamental Research Funds for the Central Universities BLX201912horizontal project 2024-HXFW-327

6 · The paper itself

Abstract

This study aimed to integrate apoptosis-associated and proliferation-apoptosis-coupled transcriptomic signatures with explainable machine learning to construct an exploratory molecular classification model for psoriasis. Transcriptomic datasets GSE30999 and GSE53552 were merged as the skin-tissue training cohort, and GSE55201, a whole-blood transcriptomic dataset, was used as an independent cross-tissue external validation cohort. Differential expression analysis identified 3707 DEGs, and intersection with GeneCards apoptosis-related genes yielded 894 overlapping genes. After PPI-based hub gene selection, eight machine learning algorithms were exploratorily compared within a preselected 25-gene feature space. DALEX-based permutation feature importance analysis identified a five-gene apoptosis-associated and proliferation-apoptosis-coupled signature comprising

Indexed as

ApoptosisMachine LearningPsoriasisTranscriptomeCell ProliferationGene Expression ProfilingHumansapoptosisexplainable machine learninggradient boosting machinepsoriasistranscriptomic classification

Identifiers

PMID42353157

DOI10.3390/ijms27125441

PMCPMC13299817

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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.