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| Natura: | Recurso digital |
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Zenodo
2026
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| Accesso online: | https://doi.org/10.5281/zenodo.18308121 |
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| _version_ | 1866901734182354944 |
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| author | Yabin Liu |
| author_facet | Yabin Liu |
| contents | <p>This release provides the version of the <strong><em>DRUGseq-PerturbFormer</em></strong> framework used in the analyses reported in the accompanying manuscript. The code implements a deep learning–based model for predicting transcriptional responses to metabolite perturbations from DRUG-seq data. This release corresponds to the version used for all analyses and results presented in the study.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18308121 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Bingobin/DRUGseq-PerturbFormer: DPF v1.0.1 Yabin Liu <p>This release provides the version of the <strong><em>DRUGseq-PerturbFormer</em></strong> framework used in the analyses reported in the accompanying manuscript. The code implements a deep learning–based model for predicting transcriptional responses to metabolite perturbations from DRUG-seq data. This release corresponds to the version used for all analyses and results presented in the study.</p> |
| title | Bingobin/DRUGseq-PerturbFormer: DPF v1.0.1 |
| url | https://doi.org/10.5281/zenodo.18308121 |