CAREER: Support Vector Methods for Functional Genomic Analysis

随着人类基因组计划接近完成，对功能基因组分析的需求日益增长，除了主要基因组序列外，还涉及其他类型的数据，如微阵列杂交实验的基因表达测量。功能基因组学的研究涉及一系列计算问题，包括可视化、聚类、分类、回归、知识表示和预测建模。该项目将涉及这些领域，但主要重点是开发机器学习技术，学习将基因放入离散的功能类别，以简化并使从基因组数据推断基因功能的问题更容易处理。最后，PI将建立在他之前的工作基础上，该工作表明支持向量机（SVM）可以使用DNA微阵列表达数据成功训练以识别各种基因功能类别，并将开发将编码序列，启动子区域，基因表达和其他类型的基因组数据结合在基于SVM的学习算法中的方法。这项研究将提高人们对各种机器学习技术识别不同类型基因功能类的能力的理解，也将产生从多种类型的数据中同时学习的新技术。从异构数据集中学习是人工智能和机器学习的核心问题；结合来自不同类型基因组数据的知识的能力对于在分子水平上理解细胞是至关重要的，并且应该导致对基因功能的重要见解。

As the Human Genome Project nears completion the need grows for functional genomic analyses which in addition to the primary genomic sequence involve other types of data such as gene expression measurements from microarray hybridization experiments. Research in functional genomics involves a range of computational problems including visualization, clustering, classification, regression, knowledge representation, and predictive modeling. This project will touch on each of these areas, but the primary focus will be on developing machine learning techniques that learn to place genes into discrete functional categories in order to simplify and render more tractable the problem of inferring gene function from genomic data. To the end the PI will build on his prior work which showed that a support vector machine (SVM) can be successfully trained using DNA microarray expression data to recognize various gene functional categories, and will develop methods for combining coding sequence, promoter region, gene expression, and other types of genomic data in SVM-based learning algorithms. The research will lead to improved understanding of the ability of various machine learning techniques to recognize different types of gene functional classes, and will also yield new techniques for learning simultaneously from multiple types of data. Learning from heterogeneous data sets is a core issue in artificial intelligence and machine learning; the ability to combine knowledge from various types of genomic data is critical for understanding the cell at the molecular level, and should lead to important insights into gene function.