English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/207015
Share/Impact:
Statistics
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Title

A guide on deep learning for complex trait genomic prediction

AuthorsPérez-Enciso, Miguel ; Zingaretti, Laura M.
KeywordsDeep learning
Genomic prediction
Machine learning
Issue Date2019
PublisherMultidisciplinary Digital Publishing Institute
CitationGenes 10(7): 553 (2019)
AbstractDeep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoretical foundations of DL and provide a generic code that can be easily modified to suit specific needs. DL comprises a wide variety of algorithms which depend on numerous hyperparameters. Careful optimization of hyperparameter values is critical to avoid overfitting. Among the DL architectures currently tested in genomic prediction, convolutional neural networks (CNNs) seem more promising than multilayer perceptrons (MLPs). A limitation of DL is in interpreting the results. This may not be relevant for genomic prediction in plant or animal breeding but can be critical when deciding the genetic risk to a disease. Although DL technologies are not “plug-and-play”, they are easily implemented using Keras and TensorFlow public software. To illustrate the principles described here, we implemented a Keras-based code in GitHub.
DescriptionThis article belongs to the Special Issue Genomic Prediction Methods for Sequencing Data.
Publisher version (URL)https://doi.org/10.3390/genes10070553
URIhttp://hdl.handle.net/10261/207015
DOI10.3390/genes10070553
E-ISSN2073-4425
Appears in Collections:(CRAG) Artículos
Files in This Item:
File Description SizeFormat 
guidepredic.pdf1,98 MBAdobe PDFThumbnail
View/Open
Show full item record
Review this work
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.