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Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data
The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10236827/ https://www.ncbi.nlm.nih.gov/pubmed/37268960 http://dx.doi.org/10.1186/s13321-023-00723-x |