Rank aggregation methods
Rank aggregation methods
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DOI：
10.1002/wics.111
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发表时间：
2010-09-01
影响因子：
3.2
通讯作者：
Lin, Shili
中科院分区：
文献类型：
--
作者：
Lin, Shili
本文概述了秩聚合方法和算法，重点是现代生物学应用。传统上，等级聚合方法广泛用于市场营销和广告研究，以及一般的应用心理学。近年来，等级聚合方法已经成为一个重要的工具，结合信息从不同的互联网搜索引擎或从不同的组学规模的生物学研究。我们讨论三类方法，即基于分布的，启发式和随机搜索。原始的Thurstone的缩放及其扩展代表了最适合聚合许多短排名列表的第一类方法。从消费者对产品的排名中汇总结果福尔斯这一类问题。它在生物学问题上的应用也在探索中。另一方面，启发式算法和随机搜索方法适用于聚集少量长列表的情况，即所谓的“高级”元分析场景。将来自不同搜索引擎/标准的结果和一些组学规模的生物学应用程序组合在一起就属于这一类。启发式算法本质上是确定性的，从简单的算术平均值到马尔可夫链和平稳分布。另一方面，随机搜索算法的目标是最大化特定的标准，例如遵循Kemeny准则的标准。将提供几个例子来说明，比较和对比的方法和算法。这些例子的范围从简单和设计，以代表现实的情况。特别是，提供了聚合基因表达微阵列研究结果的应用，以展示该方法在现代生物学问题中的应用。(C)John Wiley & Sons，Inc.
This article provides an overview of rank aggregation methods and algorithms, with an emphasis on modern biological applications. Rank aggregation methods have traditionally been used extensively in marketing and advertisement research, and in applied psychology in general. In recent years, rank aggregation methods have emerged as an important tool for combining information from different Internet search engines or from different omics-scale biological studies. We discuss three classes of methods, namely distributional based, heuristic, and stochastic search. The original Thurstone's scaling and its extensions represent the first class of methods that are most appropriate for aggregating many short ranked lists. Aggregating results from consumer rankings of products falls into this category of problems. Its application to biological problems is also being explored. On the other hand, heuristic algorithms and stochastic search methods are applicable to the situation of aggregating a small number of long lists, the so-called 'high-level' meta-analysis scenario. Combining results from different search engines/criteria and a number of omics-scale biological applications fall into this category. Heuristic algorithms are deterministic in nature, ranging from simple arithmetic averages of ranks to Markov chains and stationary distributions. Stochastic search algorithms, on the other hand, aim at maximizing a particular criterion such as that following the Kemeny guideline. Several examples will be provided to illustrate, compare, and contrast the methods and algorithms. The examples range from simple and contrive to representing realistic scenarios. In particular, an application to aggregating results from gene expression microarray studies is provided to demonstrate applications of the methods to modern biological problems. (C) 2010 John Wiley & Sons, Inc.