Horse breed discrimination using machine learning methods

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F26788462%3A_____%2F09%3A%230000267" target="_blank" >RIV/26788462:_____/09:#0000267 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985904:_____/09:00340630

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Horse breed discrimination using machine learning methods

Popis výsledku v původním jazyce

Genetic relationships and population structure of 8 horse breeds in the Czech and Slovak Republics were investigated using classification methods for breed discrimination. To demonstrate genetic differences among these breeds, we used genetic information? genotype data of microsatellite markers and classification algorithms ? to perform a probabilistic prediction of an individual?s breed. In total, 932 unrelated animals were genotyped for 17 microsatellite markers recommended by the ISAG for parentagetesting. Algorithms of classification methods ? J48 (decision trees); Naive Bayes, Bayes Net (probability predictors); IB1, IB5 (instance-based machine learning methods); and JRip (decision rules) ? were used for analysis of their classification performance and of results of classification on this genotype dataset. Selected classification methods (Naive Bayes, Bayes Net, IB1), based on machine learning and principles of artificial intelligence, appear usable for these tasks.

Název v anglickém jazyce

Horse breed discrimination using machine learning methods

Popis výsledku anglicky

Genetic relationships and population structure of 8 horse breeds in the Czech and Slovak Republics were investigated using classification methods for breed discrimination. To demonstrate genetic differences among these breeds, we used genetic information? genotype data of microsatellite markers and classification algorithms ? to perform a probabilistic prediction of an individual?s breed. In total, 932 unrelated animals were genotyped for 17 microsatellite markers recommended by the ISAG for parentagetesting. Algorithms of classification methods ? J48 (decision trees); Naive Bayes, Bayes Net (probability predictors); IB1, IB5 (instance-based machine learning methods); and JRip (decision rules) ? were used for analysis of their classification performance and of results of classification on this genotype dataset. Selected classification methods (Naive Bayes, Bayes Net, IB1), based on machine learning and principles of artificial intelligence, appear usable for these tasks.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

EB - Genetika a molekulární biologie

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

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

Název periodika

Journal of Applied Genetics

ISSN

1234-1983

e-ISSN

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Svazek periodika

50

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

3

Strana od-do

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Kód UT WoS článku

000272065300008

EID výsledku v databázi Scopus

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