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Pàgina inicial > Articles > Articles publicats > A guide for using deep learning for complex trait genomic prediction |
Data: | 2019 |
Resum: | Deep 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. |
Ajuts: | Ministerio de Economía y Competitividad AGL2016-78709-R Ministerio de Economía y Competitividad BFU2016-77236-P Ministerio de Economía y Competitividad SEV-2015-0533 |
Drets: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. |
Llengua: | Anglès |
Document: | Article ; recerca ; Versió publicada |
Matèria: | Deep learning ; Genomic prediction ; Machine learning |
Publicat a: | Genes, Vol. 10, Issue 7 (July 2019) , art. 553, ISSN 2073-4425 |
19 p, 2.0 MB |