Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F25%3A43899038" target="_blank" >RIV/44555601:13440/25:43899038 - isvavai.cz</a>

Result on the web

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001414857100009" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001414857100009</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2025.3535999" target="_blank" >10.1109/ACCESS.2025.3535999</a>

Result language

angličtina

Original language name

Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems

Original language description

The manuscript details the outcomes of a comprehensive study on the application of cluster-bicluster analysis, gene ontology analysis, and convolutional neural network (CNN) for diagnosing cancer and Alzheimer&apos;s disease using gene expression data derived from both DNA microarray experiments and mRNA sequencing. It outlines a conceptual framework and provides a block diagram of the stepwise procedure for analyzing gene expression data, aiming to enhance the accuracy and objectivity of disease diagnosis. The research methodology involves initial gene ontology analysis, followed by the application of the Self Organizing Tree Algorithm (SOTA) for clustering gene expression profiles, an ensemble algorithm for data biclustering, and CNN for sample classification. Bayesian optimization method was employed to determine the optimal hyperparameters for all models. The analysis of simulation results demonstrates the high efficacy of the proposed approach. Specifically, for Alzheimer&apos;s data, the number of genes analyzed was reduced from 44,662 to 4,004. Subsequent cluster-bicluster analysis divided this data into two subsets containing 1,158 and 2,846 genes, respectively. Classification accuracy for samples within these subsets reached 89.8% and 91.8%. In cancer data analysis, the gene count was reduced from 60,660 to 10,422, with 3,955 and 6,467 genes in the first and second clusters, respectively. The classification accuracy for these subsets was 97.4% and 97.6%, respectively. To our mind, the implementation of this model promises to significantly improve the efficacy of early diagnosis systems for complex diseases.

Czech name

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Czech description

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Type

J_imp - 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)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

IEEE Access

ISSN

2169-3536

e-ISSN

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Volume of the periodical

2025

Issue of the periodical within the volume

13

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

21265-21278

UT code for WoS article

001414857100009

EID of the result in the Scopus database

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