Technique of gene expression profiles selection based on sota clustering algorithm using statistical criteria and shannon entropy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F20%3A43895546" target="_blank" >RIV/44555601:13440/20:43895546 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-030-54215-3" target="_blank" >https://doi.org/10.1007/978-3-030-54215-3</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-54215-3_2" target="_blank" >10.1007/978-3-030-54215-3_2</a>

Result language

angličtina

Original language name

Technique of gene expression profiles selection based on sota clustering algorithm using statistical criteria and shannon entropy

Original language description

In this paper, we have presented a results of the research concerning selection of informative genes based on the complex use of both statistical criteria and Shannon entropy. The main objective of the research is development a technique of selection of the groups of gene expression profiles which allow dividing correctly the investigated samples into previously known classes using results of both DNA micro array experiments or RNA molecules sequencing method. The DNA microchips of the patients which were investigated on lung cancer disease were used as the experimental data. At the first step, we have selected the informative genes in term of both the statistical criteria and Shannon entropy. The number of gene expression profiles was reduced at this step from 54675 to 21431. Then, we have performed the step by step clustering process using SOTA algorithm. The number of the obtained clusters was varied from 2 at the first clustering level to 512 at the ninth level. Finally, we have calculated the internal clustering quality criterion for investigated samples which are in each of the clusters. The less value of this criterion corresponds to higher separate ability of genes in this cluster. The proposed technique creates the conditions for development of both the diagnostic method and forecasting technique based on gene regulatory networks using results of both DNA microchip experiments or RNA molecules sequencing method

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Article name in the collection

Advances in Intelligent Systems and Computing

ISBN

978-3-030-54214-6

ISSN

2194-5357

e-ISSN

2194-5365

Number of pages

16

Pages from-to

23-38

Publisher name

Springer

Place of publication

Switzerland

Event location

Ukraine

Event date

May 25, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000614116800002