A classification approach for heterotic performance prediction based on molecular marker data

A number of statistical methods based on molecular data are currently available for assigning new inbreds to heterotic groups in maize (Zea mays L), with variable results. We conjecture that the main flaw of such models is that they do not capture the non-linear relation between parental data and pr...

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Detalles Bibliográficos
Autores principales: Ornella, Leonardo, Tapia, Elizabeth
Formato: Articulo
Lenguaje:Inglés
Publicado: 2007
Materias:
Ciencias Informáticas
Machine learning
maize
heterotic group
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/135733
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/94
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-135733
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Machine learning
maize
heterotic group
spellingShingle Ciencias Informáticas
Machine learning
maize
heterotic group
Ornella, Leonardo
Tapia, Elizabeth
A classification approach for heterotic performance prediction based on molecular marker data
topic_facet Ciencias Informáticas
Machine learning
maize
heterotic group
description A number of statistical methods based on molecular data are currently available for assigning new inbreds to heterotic groups in maize (Zea mays L), with variable results. We conjecture that the main flaw of such models is that they do not capture the non-linear relation between parental data and progeny performance. In this paper, we propose the use of supervised learning methods for handling such non-linearity. Standard and novel multiclassification methods are evaluated. Best results are obtained with the recently introduced class of multiclass, binary based,Recursive ECOC (RECOC) classifiers. RECOC classifiers are inspired in state of art Coding Theory solutions for the problem of transmitting symbols over noisy channels. For molecular marker data the noisy channel abstraction embeds the hardness of learning a classification function from noisy and scarce samples. Field data (top crosses between 26 inbreed lines and four tester populations), processed by cluster analysis in a previous work, was integrated with molecular marker data and used for training RECOC – AdaBoost Support Vector Machines RBF classifiers. A 34.10 % 3-CV error was achieved, clearly improving previously reported results on this task.
format Articulo
Articulo
author Ornella, Leonardo
Tapia, Elizabeth
author_facet Ornella, Leonardo
Tapia, Elizabeth
author_sort Ornella, Leonardo
title A classification approach for heterotic performance prediction based on molecular marker data
title_short A classification approach for heterotic performance prediction based on molecular marker data
title_full A classification approach for heterotic performance prediction based on molecular marker data
title_fullStr A classification approach for heterotic performance prediction based on molecular marker data
title_full_unstemmed A classification approach for heterotic performance prediction based on molecular marker data
title_sort classification approach for heterotic performance prediction based on molecular marker data
publishDate 2007
url http://sedici.unlp.edu.ar/handle/10915/135733
https://publicaciones.sadio.org.ar/index.php/EJS/article/view/94
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