DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41129300", "kind": "science-record", "revision": 3, "content_hash": "sha256:8d1fb1dfbd5b7ca1fde32fb96b2278317d56067f900280384f28242e2e43ed10", "created_at": "2026-11-01T20:31:10Z", "updated_at": "2026-11-01T22:14:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies.", "doi": "10.1093/bioinformatics/btaf586", "pmid": "41129300", "authors": ["Kosuke Hamazaki", "Hiroyoshi Iwata", "Koji Tsuda"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "abstract": "MOTIVATION: Differentiable programming frameworks like PyTorch and JAX revolutionized biological modeling. A foremost merit is that multiple components programmed separately can be put together so that the parameters are jointly optimized. Despite its proven value in agricultural applications, existing breeding simulators are non-differentiable, hindering integration into general deep learning systems.\nRESULTS: In this paper, we present DiffBreed, a differentiable breeding simulator. Its performance was evaluated in gradient-based optimization of a progeny allocation strategy that maximizes the genetic gain. By utilizing gradient-based optimization, DiffBreed refined progeny allocation strategies, achieving superior genetic gains compared to a non-optimized equal allocation approach. These findings highlight DiffBreed's capacity to properly calculate gradient information through automatic differentiation. With its innovative design, DiffBreed is expected to transform future breeding optimization by integrating into modern deep learning workflows.\nAVAILABILITY AND IMPLEMENTATION: The proposed automatic differentiation framework was implemented as the Python module \"DiffBreed.\" This module and all the scripts used in this study, including the gradient-based optimization, are available from the \"KosukeHamazaki/GORA-PT\" repository on GitHub, https://github.com/KosukeHamazaki/GORA-PT. While the simulated datasets in the present study are available from the same repository, the optimized results by PyTorch were not shared in this repository due to the file sizes. Instead, all datasets, including the optimized ones, will be shared in the repository on Zenodo, https://doi.org/10.5281/zenodo.14046522.", "claims": [{"id": "claim-method", "statement": "Finally, we used stochastic gradient descent (SGD) (Robbins and Monro 1951) with these derivatives to optimize the allocation parameter θ(τ) (Fig. S2, available as supplementary data at Bioinformatics online).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41129300", "locator": "sections[5] \"2.1.2 Differentiable genetic merit of next generation\" paragraph 10", "quote": "Finally, we used stochastic gradient descent (SGD) (Robbins and Monro 1951) with these derivatives to optimize the allocation parameter θ(τ) (Fig. S2, available as supplementary data at Bioinformatics online)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "This module and all the scripts used in this study, including the gradient-based optimization, are available from the “KosukeHamazaki/GORA-PT” repository on GitHub, https://github.com/KosukeHamazaki/GORA-PT. All datasets, including the optimized ones, will be shared in the repository on Zenodo, https://doi.org/10.5281/zenodo.14046522.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41129300", "locator": "sections[23] \"Data availability\" paragraph 2", "quote": "This module and all the scripts used in this study, including the gradient-based optimization, are available from the “KosukeHamazaki/GORA-PT” repository on GitHub, https://github.com/KosukeHamazaki/GORA-PT. All datasets, including the optimized ones, will be shared in the repository on Zenodo, https://doi.org/10.5281/zenodo.14046522."}], "state": "complete"}, {"id": "claim-target-result", "statement": "By utilizing gradient-based optimization, DiffBreed refined progeny allocation strategies, achieving superior genetic gains compared to a non-optimized equal allocation approach.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41129300", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "By utilizing gradient-based optimization, DiffBreed refined progeny allocation strategies, achieving superior genetic gains compared to a non-optimized equal allocation approach."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "By utilizing gradient-based optimization, DiffBreed refined progeny allocation strategies, achieving superior genetic gains compared to a non-optimized equal allocation approach.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41129300"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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