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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Reformulated Kemeny Optimal Aggregation with Application in Consensus Ranking of microRNA Targets
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Reformulated Kemeny Optimal Aggregation with Application in Consensus Ranking of microRNA Targets

机译：重新制定的Kemeny最佳聚合及其在microRNA靶点共识排名中的应用

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MicroRNAs are very recently discovered small noncoding RNAs, responsible for negative regulation of gene expression. Members of this endogenous family of small RNA molecules have been found implicated in many genetic disorders. Each microRNA targets tens to hundreds of genes. Experimental validation of target genes is a time- and cost-intensive procedure. Therefore, prediction of microRNA targets is a very important problem in computational biology. Though, dozens of target prediction algorithms have been reported in the past decade, they disagree significantly in terms of target gene ranking (based on predicted scores). Rank aggregation is often used to combine multiple target orderings suggested by different algorithms. This technique has been used in diverse fields including social choice theory, meta search in web, and most recently, in bioinformatics. Kemeny optimal aggregation (KOA) is considered the more profound objective for rank aggregation. The consensus ordering obtained through Kemeny optimal aggregation incurs minimum pairwise disagreement with the input orderings. Because of its computational intractability, heuristics are often formulated to obtain a near optimal consensus ranking. Unlike its real time use in meta search, there are a number of scenarios in bioinformatics (e.g., combining microRNA target rankings, combining disease-related gene rankings obtained from microarray experiments) where evolutionary approaches can be afforded with the ambition of better optimization. We conjecture that an ideal consensus ordering should have its total disagreement shared, as equally as possible, with the input orderings. This is also important to refrain the evolutionary processes from getting stuck to local extremes. In the current work, we reformulate Kemeny optimal aggregation while introducing a trade-off between the total pairwise disagreement and its distribution. A simulated annealing-based implementation of the proposed objective has been found effe- tive in context of microRNA target ranking. Supplementary data and source code link are available at: >http://www.isical.ac.in/bioinfo_miu/ieee_tcbb_kemeny.rar.
机译：MicroRNA是最近发现的小型非编码RNA，负责基因表达的负调控。已经发现这种内源性小RNA分子家族的成员与许多遗传疾病有关。每个microRNA靶向数十至数百个基因。对靶基因进行实验验证是一项耗时且成本高昂的程序。因此，预测microRNA靶标是计算生物学中非常重要的问题。尽管在过去十年中已报道了数十种目标预测算法，但它们在目标基因排名（基于预测分数）方面存在很大分歧。排名聚合通常用于组合由不同算法建议的多个目标排序。这项技术已用于各种领域，包括社会选择理论，网络元搜索，以及最近的生物信息学。 Kemeny最佳聚合（KOA）被认为是秩聚合的更深层目标。通过Kemeny最佳聚合获得的共识顺序导致与输入顺序的最小成对不一致。由于其计算上的难点，通常会采用启发式方法来获得接近最佳的共识等级。与它在元搜索中的实时使用不同，生物信息学中有许多情况（例如，结合microRNA目标排名，结合从微阵列实验获得的疾病相关基因排名），可以提供具有更好优化雄心的进化方法。我们推测理想的共识排序应该使其总的分歧与输入排序尽可能相同。这对于避免进化过程陷入局部极端也很重要。在当前的工作中，我们重新制定了Kemeny最优集合，同时在总成对分歧及其分布之间引入了权衡。已经发现，在microRNA靶标排序的背景下，对拟议目标的基于模拟退火的实现是有效的。补充数据和源代码链接可在以下网址获得：> http://www.isical.ac.in/bioinfo_miu/ieee_tcbb_kemeny.rar。

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