Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data

Theoretical and Applied Genetics

Volumn 124, Issue 5, 2012, Pages 825-833

  Fu, Junjie ^a   Falke, K Christin ^b   Thiemann, Alexander ^c   Schrag, Tobias A ^d   Melchinger, Albrecht E ^d   Scholten, Stefan ^c   Frisch, Matthias ^e  

^a INSTITUTE OF PLANT PROTECTION   (China)

^b UNIVERSITY OF MÜNSTER   (Germany)

^c UNIVERSITY OF HAMBURG   (Germany)

^d UNIVERSITY OF HOHENHEIM   (Germany)

^e JUSTUS LIEBIG UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; GENE EXPRESSION; LEAST SQUARES APPROXIMATIONS; LINEAR REGRESSION; PREDICTIVE ANALYTICS; SUPPORT VECTOR MACHINES;

GENE EXPRESSION DATA; GENETIC MATERIALS; HYBRID PREDICTIONS; MULTIPLE LINEAR REGRESSIONS; PARTIAL LEAST SQUARES REGRESSION; PREDICTION ACCURACY; REGRESSION METHOD; SUPPORT VECTOR MACHINE REGRESSIONS;

SUPPORT VECTOR REGRESSION;

ZEA MAYS;

ARTICLE; BIOLOGICAL MODEL; BREEDING; COMPARATIVE STUDY; GENE EXPRESSION PROFILING; GENETICS; HYBRIDIZATION; MAIZE; METABOLISM; METHODOLOGY; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINE; VALIDATION STUDY;

BREEDING; GENE EXPRESSION PROFILING; HYBRIDIZATION, GENETIC; LEAST-SQUARES ANALYSIS; MODELS, GENETIC; SUPPORT VECTOR MACHINES; ZEA MAYS;

EID: 84862277013     PISSN: 00405752     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00122-011-1747-9     Document Type: Article

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