Applying biclustering technique and gene ontology analysis for gene expression data processing

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F24%3A43898429" target="_blank" >RIV/44555601:13440/24:43898429 - isvavai.cz</a>

Result on the web

<a href="http://chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://ceur-ws.org/Vol-3675/paper2.pdf" target="_blank" >http://chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://ceur-ws.org/Vol-3675/paper2.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Applying biclustering technique and gene ontology analysis for gene expression data processing

Original language description

This study details the biclustering methods for gene expression data, focusing on the refinement of quality criteria essential for evaluating the generated bicluster structures. An internal biclustering quality criterion is introduced, leveraging mutual information evaluation across both rows and columns within a bicluster. Additionally, the research proposes a novel hybrid biclustering model, which amalgamates the ensemble biclustering algorithm with Bayesian optimization techniques to optimize the algorithm&apos;s parameters effectively. This model is grounded on a target objective function derived from the newly proposed quality criterion. Simulations carried out on gene expression data from patients afflicted with various cancer types demonstrate the efficacy of the model. Specifically, the application of the mutual information-based criterion within the objective function results in the formation of a bicluster structure comprising 18 distinct biclusters. Furthermore, the study expands upon a method that employs gene ontology analysis, facilitating the identification of subsets of significant gene expression data from bicluster analysis results. A comprehensive procedure for identifying significant gene subsets through a combination of bicluster and gene ontology analyses is executed. The evaluation of sample classification results, characterized by these significant gene subsets, underscores the method&apos;s effectiveness. The classification quality criteria exhibit relatively high values, even with a reduced number of genes, indicating the method&apos;s efficiency

Czech name

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Czech description

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Type

D - Article in proceedings

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

2024

Confidentiality

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

Article name in the collection

CEUR Workshop Proceedings

ISBN

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ISSN

1613-0073

e-ISSN

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Number of pages

15

Pages from-to

14-28

Publisher name

CEUR-WS

Place of publication

Aachen

Event location

Khmelnytskyi

Event date

Mar 28, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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