Theory and applications of sequence analysis

序列分析的理论与应用

• 批准号: 238973-2010
• 负责人: Brown, Daniel
• 金额: $ 2.48万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=455108
• 关键词: Theory; applications; sequence; analysis

Discrete sequences arrive in scientific analysis in a variety of contexts, from biological sequences such as DNA or RNA sequences, to musical sequences such as the notes found in a score, to text sequences, such as the words of a document. For naturally-occuring sequences, what is the information that evolution or other processes has embedded in them? For man-made sequences, what patterns are imposed into the sequence, deliberately or not? Common tools are used to analyze such sequences. One tool is hidden Markov models (HMMs), invented to decode speech recordings into phonemes, but whose use has spread to detecting genes in DNA sequences, identifying similarities in musical performances or scores, or classifying seismic events.

在科学分析中，离散序列出现在各种背景下，从生物序列（如DNA或RNA序列）到音乐序列（如乐谱中的音符），再到文本序列（如文档中的单词）。 对于自然发生的序列，进化或其他过程嵌入其中的信息是什么？ 对于人造序列，什么样的模式被强加到序列中，故意或无意的？ 常用工具用于分析此类序列。其中一个工具是隐马尔可夫模型（Hidden Markov Models，简称HMF），它被发明用来将语音记录解码成音素，但它的用途已经扩展到检测DNA序列中的基因，识别音乐表演或乐谱中的相似性，或对地震事件进行分类。