A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks.
A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks.
复制标题
DOI：
10.1016/j.ygeno.2010.10.003
复制
发表时间：
2011-01
期刊：
影响因子：
4.4
通讯作者：
Statnikov, Alexander
中科院分区：
文献类型：
--
作者：
Narendra, Varun;Lytkin, Nikita I.;Aliferis, Constantin F.;Statnikov, Alexander
关键词：
基因组规模调控网络的从头逆向工程是生物学和转化研究的一个越来越重要的目标。虽然最近已经为这项任务开发了许多方法，但它们的绝对和相对性能仍然知之甚少。本研究对32种计算方法/变体进行了严格的性能评估，用于基因组规模调控网络的从头逆向工程，方法是在15个高质量数据集和实验验证的机制知识的金标准中对这些方法进行基准测试。这项研究的结果表明，有些方法需要大大改进，而另一些方法则应常规使用。我们的研究结果还表明，几个单变量方法提供了一个“看门人”的性能阈值，应适用于方法开发人员评估其新颖的多变量算法的性能时。最后，本研究的结果可以用来显示实际效用，并建立日常使用的逆向工程算法的指导方针，旨在创建自动化的数据分析协议和软件系统。
De-novo reverse-engineering of genome-scale regulatory networks is an increasingly important objective for biological and translational research. While many methods have been recently developed for this task, their absolute and relative performance remains poorly understood. The present study conducts a rigorous performance assessment of 32 computational methods/variants for de-novo reverse-engineering of genome-scale regulatory networks by benchmarking these methods in 15 high-quality datasets and gold-standards of experimentally verified mechanistic knowledge. The results of this study show that some methods need to be substantially improved upon, while others should be used routinely. Our results also demonstrate that several univariate methods provide a “gatekeeper” performance threshold that should be applied when method developers assess the performance of their novel multivariate algorithms. Finally, the results of this study can be used to show practical utility and to establish guidelines for everyday use of reverse-engineering algorithms, aiming towards creation of automated data-analysis protocols and software systems.
登录
查看更多内容
影响因子：
14.9
作者：
Faith JJ;Driscoll ME;Fusaro VA;Cosgrove EJ;Hayete B;Juhn FS;Schneider SJ;Gardner TS
通讯作者：
Gardner TS
影响因子：
14.9
作者：
Barrett T;Troup DB;Wilhite SE;Ledoux P;Rudnev D;Evangelista C;Kim IF;Soboleva A;Tomashevsky M;Marshall KA;Phillippy KH;Sherman PM;Muertter RN;Edgar R
通讯作者：
Edgar R
影响因子：
1.7
作者：
Castelo, Robert;Roverato, Alberto
通讯作者：
Roverato, Alberto
影响因子：
5.8
作者：
Huttenhower, Curtis;Hibbs, Matthew A.;Troyanskaya, Olga G.
通讯作者：
Troyanskaya, Olga G.
影响因子：
9.9
作者：
通讯作者：
--