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Evaluating parameters for ligand-based modeling with random forest on sparse data sets
Ligand-based predictive modeling is widely used to generate predictive models aiding decision making in e.g. drug discovery projects. With growing data sets and requirements on low modeling time comes the necessity to analyze data sets efficiently to support rapid and robust modeling. In this study...
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| Publicado no: | J Cheminform |
|---|---|
| Principais autores: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer International Publishing
2018
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6755600/ https://ncbi.nlm.nih.gov/pubmed/30306349 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s13321-018-0304-9 |
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