FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3012909962> ?p ?o ?g. }

• W3012909962 abstract "Abstract Background Transforming large amounts of genomic data into valuable knowledge for predicting complex traits has been an important challenge for animal and plant breeders. Prediction of complex traits has not escaped the current excitement on machine-learning, including interest in deep learning algorithms such as multilayer perceptrons (MLP) and convolutional neural networks (CNN). The aim of this study was to compare the predictive performance of two deep learning methods (MLP and CNN), two ensemble learning methods [random forests (RF) and gradient boosting (GB)], and two parametric methods [genomic best linear unbiased prediction (GBLUP) and Bayes B] using real and simulated datasets. Methods The real dataset consisted of 11,790 Holstein bulls with sire conception rate (SCR) records and genotyped for 58k single nucleotide polymorphisms (SNPs). To support the evaluation of deep learning methods, various simulation studies were conducted using the observed genotype data as template, assuming a heritability of 0.30 with either additive or non-additive gene effects, and two different numbers of quantitative trait nucleotides (100 and 1000). Results In the bull dataset, the best predictive correlation was obtained with GB (0.36), followed by Bayes B (0.34), GBLUP (0.33), RF (0.32), CNN (0.29) and MLP (0.26). The same trend was observed when using mean squared error of prediction. The simulation indicated that when gene action was purely additive, parametric methods outperformed other methods. When the gene action was a combination of additive, dominance and of two-locus epistasis, the best predictive ability was obtained with gradient boosting, and the superiority of deep learning over the parametric methods depended on the number of loci controlling the trait and on sample size. In fact, with a large dataset including 80k individuals, the predictive performance of deep learning methods was similar or slightly better than that of parametric methods for traits with non-additive gene action. Conclusions For prediction of traits with non-additive gene action, gradient boosting was a robust method. Deep learning approaches were not better for genomic prediction unless non-additive variance was sizable." @default.
• W3012909962 created "2020-03-27" @default.
• W3012909962 creator A5072581622 @default.
• W3012909962 creator A5073928785 @default.
• W3012909962 creator A5086458556 @default.
• W3012909962 date "2020-02-24" @default.
• W3012909962 modified "2023-10-13" @default.
• W3012909962 title "Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes" @default.
• W3012909962 cites W1498436455 @default.
• W3012909962 cites W1678356000 @default.
• W3012909962 cites W1928998639 @default.
• W3012909962 cites W1970149620 @default.
• W3012909962 cites W1976415646 @default.
• W3012909962 cites W1988538876 @default.
• W3012909962 cites W1988790447 @default.
• W3012909962 cites W2034433729 @default.
• W3012909962 cites W2057278369 @default.
• W3012909962 cites W2067715889 @default.
• W3012909962 cites W2073569659 @default.
• W3012909962 cites W2099085143 @default.
• W3012909962 cites W2107289228 @default.
• W3012909962 cites W2111363946 @default.
• W3012909962 cites W2112796928 @default.
• W3012909962 cites W2113244577 @default.
• W3012909962 cites W2126015874 @default.
• W3012909962 cites W2130406585 @default.
• W3012909962 cites W2139852278 @default.
• W3012909962 cites W2154562310 @default.
• W3012909962 cites W2154909789 @default.
• W3012909962 cites W2154978106 @default.
• W3012909962 cites W2163468216 @default.
• W3012909962 cites W2221443338 @default.
• W3012909962 cites W2303043072 @default.
• W3012909962 cites W2331649876 @default.
• W3012909962 cites W2413617977 @default.
• W3012909962 cites W2414780268 @default.
• W3012909962 cites W2594760713 @default.
• W3012909962 cites W2732219270 @default.
• W3012909962 cites W2761231785 @default.
• W3012909962 cites W2766291756 @default.
• W3012909962 cites W2793461513 @default.
• W3012909962 cites W2793827201 @default.
• W3012909962 cites W2885150375 @default.
• W3012909962 cites W2887039631 @default.
• W3012909962 cites W2889104897 @default.
• W3012909962 cites W2897443710 @default.
• W3012909962 cites W2906471214 @default.
• W3012909962 cites W2911964244 @default.
• W3012909962 cites W2912218317 @default.
• W3012909962 cites W2945103729 @default.
• W3012909962 cites W2963721881 @default.
• W3012909962 cites W3102476541 @default.
• W3012909962 doi "https://doi.org/10.1186/s12711-020-00531-z" @default.
• W3012909962 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7038529" @default.
• W3012909962 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32093611" @default.
• W3012909962 hasPublicationYear "2020" @default.
• W3012909962 type Work @default.
• W3012909962 sameAs 3012909962 @default.
• W3012909962 citedByCount "81" @default.
• W3012909962 countsByYear W30129099622020 @default.
• W3012909962 countsByYear W30129099622021 @default.
• W3012909962 countsByYear W30129099622022 @default.
• W3012909962 countsByYear W30129099622023 @default.
• W3012909962 crossrefType "journal-article" @default.
• W3012909962 hasAuthorship W3012909962A5072581622 @default.
• W3012909962 hasAuthorship W3012909962A5073928785 @default.
• W3012909962 hasAuthorship W3012909962A5086458556 @default.
• W3012909962 hasBestOaLocation W30129099621 @default.
• W3012909962 hasConcept C104317684 @default.
• W3012909962 hasConcept C105795698 @default.
• W3012909962 hasConcept C107673813 @default.
• W3012909962 hasConcept C108583219 @default.
• W3012909962 hasConcept C117251300 @default.
• W3012909962 hasConcept C119857082 @default.
• W3012909962 hasConcept C12267149 @default.
• W3012909962 hasConcept C154945302 @default.
• W3012909962 hasConcept C169258074 @default.
• W3012909962 hasConcept C207201462 @default.
• W3012909962 hasConcept C33923547 @default.
• W3012909962 hasConcept C41008148 @default.
• W3012909962 hasConcept C45942800 @default.
• W3012909962 hasConcept C46686674 @default.
• W3012909962 hasConcept C50644808 @default.
• W3012909962 hasConcept C52001869 @default.
• W3012909962 hasConcept C54355233 @default.
• W3012909962 hasConcept C60908668 @default.
• W3012909962 hasConcept C61727976 @default.
• W3012909962 hasConcept C70153297 @default.
• W3012909962 hasConcept C81941488 @default.
• W3012909962 hasConcept C86803240 @default.
• W3012909962 hasConceptScore W3012909962C104317684 @default.
• W3012909962 hasConceptScore W3012909962C105795698 @default.
• W3012909962 hasConceptScore W3012909962C107673813 @default.
• W3012909962 hasConceptScore W3012909962C108583219 @default.
• W3012909962 hasConceptScore W3012909962C117251300 @default.
• W3012909962 hasConceptScore W3012909962C119857082 @default.
• W3012909962 hasConceptScore W3012909962C12267149 @default.
• W3012909962 hasConceptScore W3012909962C154945302 @default.
• W3012909962 hasConceptScore W3012909962C169258074 @default.
• W3012909962 hasConceptScore W3012909962C207201462 @default.

• next