Sequential pattern induction for bioinformatics

生物信息学的序列模式归纳

• 批准号: 203139-2007
• 负责人: Kremer, Stefan
• 金额: $ 1.31万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=365431
• 关键词: Sequential; pattern; induction; bioinformatics

The proposed research applies a number of machine learning methods to problems of bioinformatics.Emphasis is on learning algorithms that learn to classify, generate and transmute sequences of symbols basedon example sequences. Over the past 10 years a number of new approaches to inducing sequences have beenproposed by myself and other researchers in the field. These have been sparsely applied to selectedbioinformatics problems and analysed.The objectives of this research are to discover which techniques work best on which problems, and how thesemethods can be adapted to the unique constraints arrising from practical problems on real biological datasets.This will allow scientists working in bioinformatics to judiciously apply these techniques to problems ofinterest as well as provide some insight into alternative approaches that might not otherwise be considered. Itwill effect an easier transfer of knowledge from research in machine learning to practical bioinformaticsproblems.The research will focus on (but not be limited to) the following techniques: artificial neural networks,automata models, formal grammars and hidden Markov models.The research is a follow-up to-- and extension of-- previous work in this area at a much enhanced scale. Thisincrease is due in part to the recent accredidation of my department to grant PhD degrees and three PhDstudents under my supervision.

该研究将许多机器学习方法应用于生物信息学问题，重点是学习算法，学习如何基于示例序列对符号序列进行分类、生成和转换。在过去的10年里，我和该领域的其他研究人员提出了一些新的诱导序列的方法。这项研究的目的是发现哪些技术对哪些问题最有效，以及这些方法如何适应实际生物数据上的实际问题产生的独特约束。这将使从事生物信息学工作的科学家明智地将这些技术应用于感兴趣的问题，并为其他可能不被考虑的替代方法提供一些见解。该研究将集中于(但不限于)以下技术：人工神经网络、自动机模型、形式文法和隐马尔可夫模型。该研究是对该领域前人工作的进一步发展和扩展。这一增长的部分原因是我的系最近授权授予博士学位，并在我的指导下授予三名博士生。