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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Weighted Markov Chain Based Aggregation of Biomolecule Orderings
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Weighted Markov Chain Based Aggregation of Biomolecule Orderings

机译：基于加权马尔可夫链的生物分子有序聚合

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摘要

The scope and effectiveness of Rank Aggregation (RA) have already been established in contemporary bioinformatics research. Rank aggregation helps in meta-analysis of putative results collected from different analytic or experimental sources. For example, we often receive considerably differing ranked lists of genes or microRNAs from various target prediction algorithms or microarray studies. Sometimes combining them all, in some sense, yields more effective ordering of the set of objects. Also, assigning a certain level of confidence to each source of ranking is a natural demand of aggregation. Assignment of weights to the sources of orderings can be performed by experts. Several rank aggregation approaches like those based on Markov Chains (MCs), evolutionary algorithms, etc., exist in the literature. Markov chains, in general, are faster than the evolutionary approaches. Unlike the evolutionary computing approaches Markov chains have not been used for weighted aggregation scenarios. This is because of the absence of a formal framework of Weighted Markov Chain (WMC). In this paper, we propose the use of a modified version of MC4 (one of the Markov chains proposed by Dwork et al., 2001), followed by the weighted analog of local Kemenization for performing rank aggregation, where the sources of rankings can be prioritized by an expert. Effectiveness of the weighted Markov chain approach over the very recently proposed Genetic Algorithm (GA) and Cross-Entropy Monte Carlo (MC) algorithm-based techniques, has been established for gene orderings from microarray analysis and orderings of predicted microRNA targets.
机译：等级聚合（RA）的范围和有效性已经在当代生物信息学研究中确立。等级汇总有助于对从不同分析或实验来源收集的推定结果进行荟萃分析。例如，我们经常从各种目标预测算法或微阵列研究中收到基因或microRNA的排名差异很大的列表。从某种意义上说，有时将它们全部组合起来，可以对对象集进行更有效的排序。同样，给每个排名来源分配一定程度的置信度是聚合的自然需求。权重分配给订单来源可以由专家执行。文献中存在几种基于Markov链（MC），进化算法等的秩聚合方法。一般而言，马尔可夫链比进化方法要快。与进化计算方法不同，马尔可夫链尚未用于加权聚合方案。这是因为缺少加权马尔可夫链（WMC）的正式框架。在本文中，我们建议使用MC4的修改版本（Dwork等人，2001年提出的马尔可夫链之一），然后使用局部Kemenization的加权类似物进行排名聚合，其中排名来源可以是由专家优先处理。已经建立了加权马尔可夫链方法对最近提出的基于遗传算法（GA）和基于交叉熵蒙特卡洛（MC）算法的技术的有效性，用于从微阵列分析和预测的microRNA靶标进行基因排序。

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