Noble William S, Kuang Rui, Leslie Christina, Weston Jason
Department of Genome Sciences Department of Computer Science and Engineering University of Washington Seattle, WA, USA.
FEBS J. 2005 Oct;272(20):5119-28. doi: 10.1111/j.1742-4658.2005.04947.x.
Perhaps the most widely used applications of bioinformatics are tools such as psi-blast for searching sequence databases. We describe a recently developed protein database search algorithm called rankprop. rankprop relies upon a precomputed network of pairwise protein similarities. The algorithm performs a diffusion operation from a specified query protein across the protein similarity network. The resulting activation scores, assigned to each database protein, encode information about the global structure of the protein similarity network. This type of algorithm has a rich history in associationist psychology, artificial intelligence and web search. We describe the rankprop algorithm and its relatives, and we provide evidence that the algorithm successfully improves upon the rankings produced by psi-blast.
也许生物信息学应用最为广泛的工具是诸如PSI-BLAST之类用于搜索序列数据库的工具。我们描述了一种最近开发的名为RankProp的蛋白质数据库搜索算法。RankProp依赖于预先计算的成对蛋白质相似性网络。该算法从指定的查询蛋白质开始在蛋白质相似性网络上执行扩散操作。分配给每个数据库蛋白质的最终激活分数编码了有关蛋白质相似性网络全局结构的信息。这类算法在联想主义心理学、人工智能和网络搜索方面有着丰富的历史。我们描述了RankProp算法及其相关算法，并提供证据表明该算法成功改进了PSI-BLAST产生的排名。
