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(2017) Automatic Differentiation in PyTorch."},{"key":"10.1016\/j.cels.2026.101701_bib78","unstructured":"Falcon, W., and The PyTorch Lightning team. (2024). PyTorch Lightning (Zenodo). https:\/\/doi.org\/10.5281\/ZENODO.13254264."},{"key":"10.1016\/j.cels.2026.101701_bib79","article-title":"Improved predictions of phase behaviour of intrinsically disordered proteins by tuning the interaction range","volume":"2","author":"Tesei","year":"2022","journal-title":"Open Res. Eur."},{"key":"10.1016\/j.cels.2026.101701_bib80","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. 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