Detail publikace

Dynamic metabolomic prediction based on genetic variation for Hordeum vulgare

NEMČEKOVÁ, P. SCHWARZEROVÁ, J.

Originální název

Dynamic metabolomic prediction based on genetic variation for Hordeum vulgare

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Hordeum vulgare, like many other crops, suffers from the reduction of genetic diversity caused by climate changes. Therefore, it is necessary to improve the performance of its breeding. Nowadays, the area of interest in current research focuses on indirect selection methods based on computational prediction modeling. This study deals with dynamic metabolomic prediction based on genomic data consisting of 33,005 single nucleotide polymorphisms. Metabolomic data include 128 metabolites belonging to 25 Halle exotic barley families. The main goal of this study is creating dynamic metabolomic predictions using different approaches chosen upon various publications. Our created models will be helpful for the prediction of phenotype or for revealing important traits of Hordeum vulgare.

Klíčová slova

Machine learning, Single nucleotide polymorphism, genomic prediction, Hordeum vulgare

Autoři

NEMČEKOVÁ, P.; SCHWARZEROVÁ, J.

Vydáno

26. 4. 2022

Nakladatel

Brno University of Technology, Faculty of Electrical Engineering and Communication

Místo

Brno

ISBN

978-80-214-6029-4

Kniha

Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers

Číslo edice

1

Strany od

251

Strany do

254

Strany počet

4

URL

https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf

BibTex

@inproceedings{BUT179425,
  author="Petra {Nemčeková} and Jana {Schwarzerová}",
  title="Dynamic metabolomic prediction based on genetic variation for Hordeum vulgare",
  booktitle="Proceedings I of the 28th Conference STUDENT EEICT 2022 General Papers",
  year="2022",
  number="1",
  pages="251--254",
  publisher="Brno University of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  isbn="978-80-214-6029-4",
  url="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_1_v2.pdf"
}