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Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding

1
CREA Research Centre for Vegetable and Ornamental Crops, 84098 Pontecagnano Faiano, Italy
2
Department of Agricultural Sciences, University of Naples Federico II, 80055 Portici, Italy
*
Author to whom correspondence should be addressed.
Plants 2020, 9(1), 34; https://doi.org/10.3390/plants9010034
Received: 5 November 2019 / Revised: 17 December 2019 / Accepted: 23 December 2019 / Published: 25 December 2019
(This article belongs to the Section Plant Genetics and Genomics)
Crops are the major source of food supply and raw materials for the processing industry. A balance between crop production and food consumption is continually threatened by plant diseases and adverse environmental conditions. This leads to serious losses every year and results in food shortages, particularly in developing countries. Presently, cutting-edge technologies for genome sequencing and phenotyping of crops combined with progress in computational sciences are leading a revolution in plant breeding, boosting the identification of the genetic basis of traits at a precision never reached before. In this frame, machine learning (ML) plays a pivotal role in data-mining and analysis, providing relevant information for decision-making towards achieving breeding targets. To this end, we summarize the recent progress in next-generation sequencing and the role of phenotyping technologies in genomics-assisted breeding toward the exploitation of the natural variation and the identification of target genes. We also explore the application of ML in managing big data and predictive models, reporting a case study using microRNAs (miRNAs) to identify genes related to stress conditions. View Full-Text
Keywords: genotyping by sequencing; genome-wide association studies; QTLs dissection; genomics; nanopore; PacBio; phenomics; machine learning; microRNA genotyping by sequencing; genome-wide association studies; QTLs dissection; genomics; nanopore; PacBio; phenomics; machine learning; microRNA
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Esposito, S.; Carputo, D.; Cardi, T.; Tripodi, P. Applications and Trends of Machine Learning in Genomics and Phenomics for Next-Generation Breeding. Plants 2020, 9, 34.

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