Evaluating proximity metrics for gene expression data: A hybrid model integrating data mining and machine learning techniques for disease diagnosis systems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F25%3A43899179" target="_blank" >RIV/44555601:13440/25:43899179 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1746809425006263" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1746809425006263</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.bspc.2025.108115" target="_blank" >10.1016/j.bspc.2025.108115</a>
Alternative languages
Result language
angličtina
Original language name
Evaluating proximity metrics for gene expression data: A hybrid model integrating data mining and machine learning techniques for disease diagnosis systems
Original language description
This study presents the development and application of a hybrid model for evaluating proximity metrics in high-dimensional gene expression data, integrating data mining and machine learning methods within a comprehensive framework. The research focuses on the comparative analysis of correlation distance, mutual information-based and Wasserstein metrics, assessing their effectiveness for clustering and classification tasks. In the initial modeling stage using gene expression data from over 6,000 patient samples covering 13 cancer types (TCGA dataset), the proposed model achieved classification accuracy exceeding 95.9% and a weighted F1-score above 95.8%. External validation using Alzheimer's (GSE174367) and Type 2 Diabetes (GSE81608) datasets confirmed the model's generalizability, with accuracy values reaching 96.28% and 97.43%, and weighted F1-scores of 96.26% and 97.41%, respectively. A stacking model was implemented to enhance classification robustness, compensating for potential clustering errors and delivering consistent performance across varying metrics and cluster structures. The proposed data processing pipeline ensures automated, standardized, and scalable analysis of large-scale gene expression datasets, aligning with the principles of personalized medicine.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biomedical signal processing and control
ISSN
1746-8094
e-ISSN
1746-8108
Volume of the periodical
2025
Issue of the periodical within the volume
110
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
108-115
UT code for WoS article
001529361000002
EID of the result in the Scopus database
2-s2.0-105009419762