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Predicting phenotypes from genetic, environment, management, and historical data using CNNs.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik - 1 Dec 2021

Washburn Jacob D, Cimen Emre, Ramstein Guillaume, Reeves Timothy, O'Briant Patrick, McLean Greg, Cooper Mark, Hammer Graeme, Buckler Edward S

Abstract excerpt

KEY MESSAGE: Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a...

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Identifiers

PMID: 34448888
DOI: 10.1007/s00122-021-03943-7

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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