The Statistical Evaluation Of Retrieval Efficiency From A Database.

随着生物信息的增长，数据库检索的效率已经成为生物企业的核心。特别是，人们可以改变检索方法，并且必须能够评估这种改变是否是一种改进。最初，使用U-统计量，我们开发的中心极限定理描述的行为下自举的接收器操作特征曲线n（ROCn）。我们的方法被应用于确定PSI-BLAST程序的哪些变化实际上构成了改进。然而，最终，我们拒绝了ROCn作为生物信息学数据库检索效率的不可接受的衡量标准，取而代之的是TAPk。通过测量每个查询的检索效率（ROCn不能总是这样做），TAPk允许检索方法的度量，以确定两种检索方法通过其行为查询的密切相关程度。度量可以区分，例如，从一个真正新颖的算法（产生一个“遥远的”检索）对一个公认的检索算法（产生“接近”该算法的检索）进行“调整”，从而通过客观地展示现有算法的增量改进来奖励生物信息学中的原创性。

With the growth of biological information, the efficiency of database retrieval has become central to the biological enterprise. In particular, one can change a retrieval method and must be able to evaluate whether the change is an improvement or not. Initially, using U-statistics, we developed central limit theorems describing the behavior of the receiver operating characteristic curve n (ROCn) under bootstrapping. Our methodology was applied to determine which changes to the PSI-BLAST program actually constitute improvements. Eventually, however, we rejected the ROCn as an unacceptable measure of database retrieval efficacy for bioinformatics, substituting in its place the TAPk. By measuring the retrieval efficacy for each query (which the ROCn cannot always do), the TAPk permits metrics on retrieval methods, to determine how closely related two retrieval methods are by their behavior query by query. The metrics can distinguish, e.g., a "tweak" on an accepted retrieval algorithm (which produces retrieval "close" to the algorithm's) from a truly novel algorithm (which produces a "distant" retrieval), thereby rewarding originality in bioinformatics, by objectively displaying incremental improvements of existing algorithms for what they are.