Criterial Analysis of Gene Expression Sequences to Create the Objective Clustering Inductive Technology

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F17%3A43892884" target="_blank" >RIV/44555601:13440/17:43892884 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ELNANO.2017.7939756" target="_blank" >http://dx.doi.org/10.1109/ELNANO.2017.7939756</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ELNANO.2017.7939756" target="_blank" >10.1109/ELNANO.2017.7939756</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Criterial Analysis of Gene Expression Sequences to Create the Objective Clustering Inductive Technology

Popis výsledku v původním jazyce

The paper presents the researches to determine the effectiveness of different criteria to estimate the complex biology objects clustering quality. The gene expression sequences of cancer patients were used as experimental data. The degree of the studied objects similarity was estimated by the comparison of the gene expression sequences profile using different metrics to estimate the objects proximity. The studies have shown that the best separating ability is obtained by using the correlation metric proximity of objects. Herewith the use of the CH criterion (Calinski-Harabasz) allows to get the most objective objects clustering by using simulated data. The presented research is focused mainly on the inductive model of the objective clustering, where the objects clustering is carried out concurrently on the two equal power subsets. In this case, the final decision about the objects grouping is accepted using the two subsets basing both on the internal clustering quality criteria estimating and the minimum value of the external criterion of clustering similarity.

Název v anglickém jazyce

Criterial Analysis of Gene Expression Sequences to Create the Objective Clustering Inductive Technology

Popis výsledku anglicky

The paper presents the researches to determine the effectiveness of different criteria to estimate the complex biology objects clustering quality. The gene expression sequences of cancer patients were used as experimental data. The degree of the studied objects similarity was estimated by the comparison of the gene expression sequences profile using different metrics to estimate the objects proximity. The studies have shown that the best separating ability is obtained by using the correlation metric proximity of objects. Herewith the use of the CH criterion (Calinski-Harabasz) allows to get the most objective objects clustering by using simulated data. The presented research is focused mainly on the inductive model of the objective clustering, where the objects clustering is carried out concurrently on the two equal power subsets. In this case, the final decision about the objects grouping is accepted using the two subsets basing both on the internal clustering quality criteria estimating and the minimum value of the external criterion of clustering similarity.

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)

Projekt

—

Návaznosti

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

2017 IEEE 37TH INTERNATIONAL CONFERENCE ON ELECTRONICS AND NANOTECHNOLOGY (ELNANO)

ISBN

978-1-5386-1701-4

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

5

Strana od-do

244-248

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Kyiv, UKRAINE

Datum konání akce

18. 4. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000403399800053