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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