A guide for using deep learning for complex trait genomic prediction

Miguel Pérez-Enciso, Laura M. Zingaretti

Research output: Contribution to journalReview articleResearchpeer-review

23 Citations (Scopus)

Abstract

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. 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.
Original languageEnglish
Article number553
JournalGenes
Volume10
Issue number7
DOIs
Publication statusPublished - 1 Jul 2019

Keywords

  • Deep learning
  • Genomic prediction
  • Machine learning

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