{
  "abstract": "As climate change reshapes grape-growing conditions, predicting grapevine phenotypes from genomic variation is increasingly important for identifying and breeding cultivars suited to emerging environmental pressures. Existing research on the grapevine genotype-phenotype relationship using machine learning has often focused on individual traits, isolated regions, or a limited set of predictive models. We introduce VitisGP, a multi-phenotype curated dataset, integrating genome-wide SNP data with 26 heterogeneous phenotype prediction tasks spanning sommelier-curated aroma traits (N=11), botanical traits (N=14) and wine color---across 1,197 Vitis vinifera cultivars. We employ VitisGP to benchmark phenotype prediction, comparing machine-learning models with differing inductive biases---L2-regularized logistic regression, Random Forest, XGBoost, CatBoost, GBLUP and Transformer-based models, providing the first systematic comparison of this breadth on harmonized grapevine data. Across the benchmark, CatBoost achieves the strongest overall performance (macro-average AUROC=0.696 across 26 tasks), although model performance varies substantially across phenotype classes, with sensory and color traits showing stronger predictability than agronomic characteristics. VitisGP provides the community with a resource for investigating the genomic basis of phenotypic traits and screening grapevine diversity for climate-relevant characteristics.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": "Journal reference: 40th Conference on Neural Information Processing Systems (NeurIPS 2026). Workshop: Tackling Climate Change with Machine Learning",
  "arxiv": "2610.40668",
  "author_total": 3,
  "authors": [
    {
      "name": "Chiara D'Ercoli"
    },
    {
      "name": "May Levin"
    },
    {
      "name": "Pietro Liò"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cnsp4sqemx8q4.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40668",
  "fulltext_url": null,
  "id": "cnsp4sqemx8q4",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.40668",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.GN"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40668",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-13",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cnsp4sqemx8q4.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:21Z",
      "native_id": "2610.40668",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40668"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "VitisGP: Multi-Task Benchmark for Genotype to Phenotype Grapevine Prediction",
  "title_text": "VitisGP: Multi-Task Benchmark for Genotype to Phenotype Grapevine Prediction",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/cnsp4sqemx8q4",
  "venue": "arXiv",
  "versions": []
}