Evaluation of the gene expression profiles complex proximity metric effectiveness based on a hybrid technique of gene expression data extraction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F21%3A43896988" target="_blank" >RIV/44555601:13440/21:43896988 - isvavai.cz</a>

Result on the web

<a href="http://ceur-ws.org/Vol-3038/paper10.pdf" target="_blank" >http://ceur-ws.org/Vol-3038/paper10.pdf</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Evaluation of the gene expression profiles complex proximity metric effectiveness based on a hybrid technique of gene expression data extraction

Original language description

Gene expression data processing in order to develop the systems of complex diseases diagnostic or/and gene regulatory networks (GRN) reconstruction is one of the actual direction of modern bioinformatics. One of the important stages of this problem solving is an extraction of mutually correlated gene expression profiles (GEP) considering the used proximity metric. Within the framework of our research, we evaluate the complex metric of GEP proximity calculated as the combination of modified mutual information criterion and Pearson&apos;s chi-squared test using OPTICS clustering algorithm implemented using principles of the objective clustering inductive technique (OCIT). The examined objects classification accuracy was used as the main criterion to access the applied method effectiveness. The simulation results have shown that the proposed technique allows us to form an optimal GEP cluster structure in terms of maximum values of the patterns classification accuracy quality criterion.

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

2021

Confidentiality

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

Article name in the collection

Proceedings of the 4th International Conference on Informatics &amp; Data-Driven Medicine

ISBN

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ISSN

1613-0073

e-ISSN

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Number of pages

11

Pages from-to

150-160

Publisher name

RWTH AACHEN

Place of publication

Aachen

Event location

Valencia

Event date

Nov 19, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000770795000016