生物序列分析
Biological sequence analysis
摘要：
这次演讲将回顾十多年来关于将某些随机模型应用于生物序列分析的研究。这些模型本身有着更长的历史，可以追溯到30多年前，尽管从那时起出现了许多新的变体。模型在生物序列分析中的功能是总结关于生物信息学中被称为基序或结构域的信息，并提供用于在单独的序列片段中发现该基序或结构域的实例的工具。我们将分阶段介绍基序模型，从非常简单的非随机版本开始，逐渐变得更加复杂，直到我们达到现代的基序轮廓障碍。第二个例子来自于使用来自一个或两个物种的序列数据的基因发现，其中广义Hacks或广义对Hacks已被证明是非常有效的。
英文摘要：
This talk will review a little over a decade's research on applying certain stochastic models to biological sequence analysis. The models themselves have a longer history, going back over 30 years, although many novel variants have arisen since that time. The function of the models in biological sequence analysis is to summarize the information concerning what is known as a motif or a domain in bioinformatics, and to provide a tool for discovering instances of that motif or domain in a separate sequence segment. We will introduce the motif models in stages, beginning from very simple, non-stochastic versions, progressively becoming more complex, until we reach modern profile HMMs for motifs. A second example will come from gene finding using sequence data from one or two species, where generalized HMMs or generalized pair HMMs have proved to be very effective.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
