Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Journal: Bioinformatics (Oxford, England)
PMID: 37220903

Abstract

MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plant breeding programs. While methods to predict yield from genotype or phenotype data have been proposed, improved performance and integrated models are needed.

Authors

  • Matteo Togninalli
    Visium, Lausanne 1015, Switzerland.
  • Xu Wang
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907.
  • Tim Kucera
    Department of Biosystems Science and Engineering, ETH Zürich, Basel 4058, Switzerland.
  • Sandesh Shrestha
    Department of Plant Pathology, Kansas State University, 4024 Throckmorton PSC, 1712 Claflin Road, Manhattan, KS 66506, USA.
  • Philomin Juliana
    International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Ciudad de México, México.
  • Suchismita Mondal
    Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Estado de Mexico, Mexico.
  • Francisco Pinto
    International Maize and Wheat Improvement Center (CIMMYT), Carretera México- Veracruz Km. 45, El Batán, CP 56237, Texcoco, Edo. de México, Mexico.
  • Velu Govindan
    Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Estado de Mexico, Mexico.
  • Leonardo Crespo-Herrera
    Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Estado de Mexico, Mexico.
  • Julio Huerta-Espino
    Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Estado de Mexico, Mexico.
  • Ravi P Singh
    Global Wheat Program, International Maize and Wheat Improvement Center, Texcoco, Estado de Mexico, Mexico.
  • Karsten Borgwardt
    Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
  • Jesse Poland
    Department of Plant Pathology, Kansas State University, 4024 Throckmorton PSC, 1712 Claflin Road, Manhattan, KS 66506, USA.

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