Ensemble-based clustering and classification pipeline for cancer diagnosis using gene expression data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F25%3A43899385" target="_blank" >RIV/44555601:13440/25:43899385 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1746809425016441?pes=vor&utm_source=scopus&getft_integrator=scopus" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1746809425016441?pes=vor&utm_source=scopus&getft_integrator=scopus</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2025.109133" target="_blank" >10.1016/j.bspc.2025.109133</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Ensemble-based clustering and classification pipeline for cancer diagnosis using gene expression data

Popis výsledku v původním jazyce

Objective: Accurate analysis of gene expression data is essential for understanding cancer mechanisms and improving diagnostics. However, the high dimensionality and heterogeneity of transcriptomic profiles often produce unstable clustering and classification outcomes. This study proposes an ensemble-based pipeline designed to improve robustness, interpretability, and clinical utility in cancer diagnostics. Methods: We developed a hybrid framework that integrates the Self-Organizing Tree Algorithm (SOTA) with agglomerative and spectral consensus clustering. Gene expression data from 6310 samples and 18,564 genes across 14 classes were transformed into cluster-based subsets. Classification was performed using Random Forest models with Bayesian hyperparameter optimization and out-of-fold stacking. Clustering quality was evaluated using the Relative Cluster Separation Index (RCSI), Calinski?Harabasz, Silhouette, and PBM indices, while biological interpretation was based on KEGG enrichment and Cytoscape (ClueGO/CluePedia) functional networks. Results: The spectral consensus variant of SOTA achieved the most balanced performance across all metrics. On TCGA data, three- to five-cluster configurations provided high diagnostic accuracy (Accuracy ? 0.975, F1 ? 0.976) with statistically validated improvements (p&lt;0.05) over baseline models. External validation on the independent expO dataset confirmed cross-platform robustness (Accuracy ? 0.811, F1 ? 0.843). Functional enrichment linked each cluster to biologically coherent pathways, including immune regulation, neuroactive signaling, metabolism, and viral response. Conclusion: The proposed ensemble?clustering?classification framework unifies consensus clustering, ensemble learning, and pathway-level validation, offering a reproducible and interpretable approach for cancer gene expression analysis, biomarker discovery, and precision oncology applications.

Název v anglickém jazyce

Ensemble-based clustering and classification pipeline for cancer diagnosis using gene expression data

Popis výsledku anglicky

Objective: Accurate analysis of gene expression data is essential for understanding cancer mechanisms and improving diagnostics. However, the high dimensionality and heterogeneity of transcriptomic profiles often produce unstable clustering and classification outcomes. This study proposes an ensemble-based pipeline designed to improve robustness, interpretability, and clinical utility in cancer diagnostics. Methods: We developed a hybrid framework that integrates the Self-Organizing Tree Algorithm (SOTA) with agglomerative and spectral consensus clustering. Gene expression data from 6310 samples and 18,564 genes across 14 classes were transformed into cluster-based subsets. Classification was performed using Random Forest models with Bayesian hyperparameter optimization and out-of-fold stacking. Clustering quality was evaluated using the Relative Cluster Separation Index (RCSI), Calinski?Harabasz, Silhouette, and PBM indices, while biological interpretation was based on KEGG enrichment and Cytoscape (ClueGO/CluePedia) functional networks. Results: The spectral consensus variant of SOTA achieved the most balanced performance across all metrics. On TCGA data, three- to five-cluster configurations provided high diagnostic accuracy (Accuracy ? 0.975, F1 ? 0.976) with statistically validated improvements (p&lt;0.05) over baseline models. External validation on the independent expO dataset confirmed cross-platform robustness (Accuracy ? 0.811, F1 ? 0.843). Functional enrichment linked each cluster to biologically coherent pathways, including immune regulation, neuroactive signaling, metabolism, and viral response. Conclusion: The proposed ensemble?clustering?classification framework unifies consensus clustering, ensemble learning, and pathway-level validation, offering a reproducible and interpretable approach for cancer gene expression analysis, biomarker discovery, and precision oncology applications.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Biomedical signal processing and control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

2025

Číslo periodika v rámci svazku

113

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

14

Strana od-do

"nestrankovano"

Kód UT WoS článku

001616477600001

EID výsledku v databázi Scopus

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