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LSTM-attention-guided graph neural networks for integrated genotype–Environment modeling in maize yield prediction.

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Title: LSTM-attention-guided graph neural networks for integrated genotype–Environment modeling in maize yield prediction.
Authors: Morshedian, Amir1 (AUTHOR) amir.morshedian@uwo.ca, Domaratzki, Mike1 (AUTHOR)
Source: PLoS Computational Biology. 5/20/2026, Vol. 22 Issue 5, p1-20. 20p.
Database: Academic Search Ultimate
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  Data: LSTM-attention-guided graph neural networks for integrated genotype–Environment modeling in maize yield prediction.
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  Data: <searchLink fieldCode="JN" term="%22PLoS+Computational+Biology%22">PLoS Computational Biology</searchLink>. 5/20/2026, Vol. 22 Issue 5, p1-20. 20p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=193892731
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        Value: 10.1371/journal.pcbi.1013729
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      – TitleFull: LSTM-attention-guided graph neural networks for integrated genotype–Environment modeling in maize yield prediction.
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              Text: 5/20/2026
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