| Título: | Fusing similarity rankings in ligand-based virtual screening |
| Autores: | Willett, Peter; University of Sheffield |
| Fecha: | 2013-02-24 |
| Publicador: | Computacional and structural biotechnology journal |
| Fuente: |
 |
| Tipo: |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
| Tema: | No aplica |
| Descripción: | Data fusion is the name given to a range of methods for combining multiple sources of evidence. This mini-review summarizes the use of one such class of methods for combining the rankings obtained when similarity searching is used for ligand-based virtual screening. Two main approaches are described: similarity fusion involves combining rankings from single searches based on multiple similarity measures; and group fusion involves combining rankings from multiple searches based on a single similarity measure. The review then focuses on the rules that are available for combining similarity rankings, and on the evidence that exists for the superiority of fusion-based methods over conventional similarity searching. |
| Idioma: | Inglés |
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