A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F25%3A43899103" target="_blank" >RIV/44555601:13440/25:43899103 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.mdpi.com/2076-3417/15/8/4471#:~:text=The%20present%20study%20addresses%20this%20challenge%20by%20proposing,Gene%20Ontology%20%28GO%29%20enrichment%2C%20and%20ensemble-based%20machine%20learning" target="_blank" >https://www.mdpi.com/2076-3417/15/8/4471#:~:text=The%20present%20study%20addresses%20this%20challenge%20by%20proposing,Gene%20Ontology%20%28GO%29%20enrichment%2C%20and%20ensemble-based%20machine%20learning</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app15084471" target="_blank" >10.3390/app15084471</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications

Popis výsledku v původním jazyce

The growing volume and complexity of gene expression data necessitate biologically meaningful and statistically robust methods for feature selection to enhance the effectiveness of disease diagnosis systems. The present study addresses this challenge by proposing a pipeline that integrates RNA-seq data preprocessing, differential gene expression analysis, Gene Ontology (GO) enrichment, and ensemble-based machine learning. The pipeline employs the non-parametric Kruskal-Wallis test to identify differentially expressed genes, followed by dual enrichment analysis using both Fisher&apos;s exact test and the Kolmogorov-Smirnov test across three GO categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Genes associated with GO terms found significant by both tests were used to construct multiple gene subsets, including subsets based on individual categories, their union, and their intersection. Classification experiments using a random forest model, validated via 5-fold cross-validation, demonstrated that gene subsets derived from the CC category and the union of all categories achieved the highest accuracy and weighted F1-scores, exceeding 0.97 across 14 cancer types. In contrast, subsets derived from BP, MF, and especially their intersection exhibited lower performance. These results confirm the discriminative power of spatially localized gene annotations and underscore the value of integrating statistical and functional information into gene selection. The proposed approach improves the reliability of biomarker discovery and supports downstream analyses such as clustering and biclustering, providing a strong foundation for developing precise diagnostic tools in personalized medicine.

Název v anglickém jazyce

A Gene Ontology-Based Pipeline for Selecting Significant Gene Subsets in Biomedical Applications

Popis výsledku anglicky

The growing volume and complexity of gene expression data necessitate biologically meaningful and statistically robust methods for feature selection to enhance the effectiveness of disease diagnosis systems. The present study addresses this challenge by proposing a pipeline that integrates RNA-seq data preprocessing, differential gene expression analysis, Gene Ontology (GO) enrichment, and ensemble-based machine learning. The pipeline employs the non-parametric Kruskal-Wallis test to identify differentially expressed genes, followed by dual enrichment analysis using both Fisher&apos;s exact test and the Kolmogorov-Smirnov test across three GO categories: Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). Genes associated with GO terms found significant by both tests were used to construct multiple gene subsets, including subsets based on individual categories, their union, and their intersection. Classification experiments using a random forest model, validated via 5-fold cross-validation, demonstrated that gene subsets derived from the CC category and the union of all categories achieved the highest accuracy and weighted F1-scores, exceeding 0.97 across 14 cancer types. In contrast, subsets derived from BP, MF, and especially their intersection exhibited lower performance. These results confirm the discriminative power of spatially localized gene annotations and underscore the value of integrating statistical and functional information into gene selection. The proposed approach improves the reliability of biomarker discovery and supports downstream analyses such as clustering and biclustering, providing a strong foundation for developing precise diagnostic tools in personalized medicine.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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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

Applied Sciences

ISSN

2076-3417

e-ISSN

2076-3417

Svazek periodika

15

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

16

Strana od-do

"nestrankovano"

Kód UT WoS článku

001474798600001

EID výsledku v databázi Scopus

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