A guide for using deep learning for complex trait genomic prediction
Perez-Enciso, Miguel (Centre de Recerca en Agrigenòmica)
Zingaretti, Laura M. (Centre de Recerca en Agrigenòmica)

Date:	2019
Abstract:	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.
Grants:	Ministerio de Economía y Competitividad AGL2016-78709-R Ministerio de Economía y Competitividad BFU2016-77236-P Ministerio de Economía y Competitividad SEV-2015-0533
Rights:	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.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Deep learning ; Genomic prediction ; Machine learning
Published in:	Genes, Vol. 10, Issue 7 (July 2019) , art. 553, ISSN 2073-4425

DOI: 10.3390/genes10070553
PMID: 31330861

19 p, 2.0 MB

The record appears in these collections:
Research literature > UAB research groups literature > Research Centres and Groups (research output) > Experimental sciences > CRAG (Centre for Research in Agricultural Genomics)
Articles > Research articles
Articles > Published articles