PMSprune与其他基序搜索算法的实验比较。

Dolly Sharma, Sanguthevar Rajasekaran, Sudipta Pathak
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引用次数: 5

摘要

由于几个原因,对各种基序搜索算法进行比较研究是非常重要的。例如,我们可以确定每个人的优点和缺点。因此,我们也许能够设计出比单个部件性能更好的混合动力汽车。在本文中,我们(直接或间接)比较了PMSprune(一种基于(l, d)-motif模型的算法)和其他几种算法在七个度量方面的性能,并使用了完善的基准。我们使用了几个基准数据集,包括Tompa等人使用的数据集。可以观察到,PMSprune和DME(一种基于位置特定评分矩阵的算法)总体上比Tompa等人报道的13种算法表现更好。随后,我们将PMSprune和DME在ChIP-Chip、ChIP-Seq和ABS等其他基准数据集上进行了比较。在PMSprune和DME之间,PMSprune在6个指标上的表现优于DME。DME在一项测量(即特异性)上比PMSprune表现更好。
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An experimental comparison of PMSprune and other algorithms for motif search.

A comparative study of the various motif search algorithms is very important for several reasons. For example, we could identify the strengths and weaknesses of each. As a result, we might be able to devise hybrids that will perform better than the individual components. In this paper, we (either directly or indirectly) compare the performance of PMSprune (an algorithm based on the (l, d)-motif model) and several other algorithms in terms of seven measures and using well-established benchmarks. We have employed several benchmark datasets including the one used by Tompa et al. It is observed that both PMSprune and DME (an algorithm based on position-specific score matrices), in general, perform better than the 13 algorithms reported in Tompa et al. Subsequently, we have compared PMSprune and DME on other benchmark datasets including ChIP-Chip, ChIP-Seq and ABS. Between PMSprune and DME, PMSprune performs better than DME on six measures. DME performs better than PMSprune on one measure (namely, specificity).

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来源期刊
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management
CiteScore
0.60
自引率
0.00%
发文量
26
期刊介绍: Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.
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