顺式调控模块的集成预测器

Darby Tien-Hao Chang, G. Shiu, You-Jie Sun
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引用次数: 0

摘要

在过去的十年中,各种顺式监管模块(CRM)预测器被提出。几种成熟的CRM预测方法采用了不同类型的预测策略,包括窗口聚类、概率建模和系统发育足迹。适当地整合它们有可能实现高质量的CRM预测。本研究分析了四种现有的CRM预测因子(ClusterBuster、MSCAN、CisModule和MultiModule),以寻求一种比单个CRM预测因子更准确的预测因子组合。利用来自红蝇数据库的140个黑胃果蝇基因中的465个CRM来评估本研究提出的综合CRM预测因子。结果表明,四种预测因子组合比最佳的个人CRM预测因子取得了更好的效果。关键词:顺式调控模块;转录因子结合位点;
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Integrated Predictor for Cis-Regulatory Modules
Various cis-regulatory module (CRM) predictors have been proposed in the last decade. Several well-established CRM predictors adopted different categories of prediction strategies, including window clustering, probabilistic modeling and phylogenetic footprinting. Appropriate integration of them has a potential to achieve high quality CRM prediction. This study analyzed four existing CRM predictors (ClusterBuster, MSCAN, CisModule and MultiModule) to seek a predictor combination that delivers a higher accuracy than individual CRM predictors. 465 CRMs across 140 Drosophila melanogaster genes from the RED fly database were used to evaluate the integrated CRM predictor proposed in this study. The results show that four predictor combinations achieved superior performance than the best individual CRM predictor. Keywords—Cis-regulatory module, transcription factor binding site.
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