Technique of gene expression profiles selection based on sota clustering algorithm using statistical criteria and shannon entropy
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Technique of gene expression profiles selection based on sota clustering algorithm using statistical criteria and shannon entropy
Popis výsledku v původním jazyce
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
Název v anglickém jazyce
Technique of gene expression profiles selection based on sota clustering algorithm using statistical criteria and shannon entropy
Popis výsledku anglicky
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
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2020
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Advances in Intelligent Systems and Computing
ISBN
978-3-030-54214-6
ISSN
2194-5357
e-ISSN
2194-5365
Počet stran výsledku
16
Strana od-do
23-38
Název nakladatele
Springer
Místo vydání
Switzerland
Místo konání akce
Ukraine
Datum konání akce
25. 5. 2020
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
000614116800002