间隙生物序列比对极值(Gumbel)参数的直接计算方法。

Q4 Health Professions International Journal of Bioinformatics Research and Applications Pub Date : 2014-01-01 DOI:10.1504/IJBRA.2014.059517

Terrance Quinn, Zachariah Sinkala

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引用次数: 0

摘要

我们开发了一种计算极值分布参数的通用方法(Gumbel, 1958)。我们的方法使用混合分布理论获得间隙比对的相关BLOSUM矩阵，该矩阵反过来用于确定生物序列对的间隙比对分数的重要性。我们将我们的结果与文献中已经得到的参数进行比较。

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A direct method for computing extreme value (Gumbel) parameters for gapped biological sequence alignments.

We develop a general method for computing extreme value distribution (Gumbel, 1958) parameters for gapped alignments. Our approach uses mixture distribution theory to obtain associated BLOSUM matrices for gapped alignments, which in turn are used for determining significance of gapped alignment scores for pairs of biological sequences. We compare our results with parameters already obtained in the literature.

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

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.