Explainable artificial intelligence for genotype-to-phenotype prediction in plant breeding: a case study with a dataset from an almond germplasm collection
Employing explainable artificial intelligence algorithms enhances model interpretability, identifying genetic polymorphisms associated with the shelling percentage. These findings underscore XAI's efficacy in predicting phenotypic traits from genomic data, highlighting its significance in optimizing crop production for sustainable agriculture.
Authors
- Stefano Pavan (ORCID: https://orcid.org/0000-0002-3666-7291)
- R. Bellotti (ORCID: https://orcid.org/0000-0003-3198-2708)
- Sabina Tangaro (ORCID: https://orcid.org/0000-0002-1372-3916)
- Donato Romano (ORCID: https://orcid.org/0000-0001-7120-8050)
- Pierfrancesco Novielli (ORCID: https://orcid.org/0000-0001-8773-0636)
- Domenico Diacono (ORCID: https://orcid.org/0000-0002-8060-5805)
- Pasquale Losciale (ORCID: https://orcid.org/0000-0003-2274-3246)
- Anna Maria Stellacci (ORCID: https://orcid.org/0000-0001-9689-7649)
Institutions
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari (IT)
- University of Bari Aldo Moro (IT)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2024-09-09
- DOI
- https://doi.org/10.3389/fpls.2024.1434229
- Citations
- 21
- Primary Topic
- Nuts composition and effects
- Type
- article
- Field-Weighted Citation Impact
- 6.50
Funders
- Ministero dell’Istruzione, dell’Università e della Ricerca
- Programma Operativo Nazionale Ricerca e Competitività