Statistical Methods in Bioinformatics: An Introduction
Statistical Methods in Bioinformatics: An Introduction
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DOI：
10.1007/b137845
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发表时间：
2005-01-01
期刊：
影响因子：
--
通讯作者：
Grant, G.
中科院分区：
文献类型：
--
作者：
Ewens, W.;Grant, G.
计算机和生物技术的进步对生物医学研究产生了深远的影响，因此现在可以生成复杂的数据集来解决极其复杂的生物学问题。相应地，分析这些数据所需的统计方法的进步紧随数据生成方法的进步。生物信息学所要求的统计方法为研究界提出了许多新的和困难的问题。这本书提供了一些新方法的介绍。处理的主要生物学主题包括序列分析，BLAST，微阵列分析，基因发现和进化过程的分析。主要的统计技术包括假设检验和估计，泊松过程，马尔可夫模型和隐马尔可夫模型，以及多种检验方法。
Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.