CAREER: Machine Learning with Rich Data Sources and Interrelated Tasks

职业：具有丰富数据源和相关任务的机器学习

• 批准号: 0093016
• 负责人: Mark Craven
• 金额: $ 48.7万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-05-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0093016&HistoricalAwards=false
• 关键词: CAREER; Machine; Learning; Rich; Data

Now that numerous genomes have been sequenced, a significant challenge confronting biologists is to determine the functions of the genes contained in these genomes. One aspect of understanding the function of a given gene is to determine the conditions under which it is active, the mechanisms responsible for controlling its level of activity, and the interactions it has with other genes. Toward this end, in the current project the PI will develop new computational approaches to uncovering the regulatory mechanisms and interactions of genes in a given organism. In particular, the focus is on developing new machine learning methods which are able to predictively identify various regulatory elements of a genome, using well-characterized aspects of the genome as training data. The expected impact of this research is twofold: it will produce new methods and software that can be applied by molecular biologists to gain insight into the regulatory apparatus of the cell; and it will advance the state of the art in machine learning by developing new methods for problem domains that involve (i) multiple inter-related learning tasks, (ii) rich and varied sources of data including sequence and text data, and (iii) the need for rich representation languages, such as stochastic context-free grammars and relational rules.

既然已经对许多基因组进行了测序，生物学家面临的一个重大挑战是确定这些基因组中包含的基因的功能。了解特定基因功能的一个方面是确定其活动的条件，控制其活动水平的机制，以及它与其他基因的相互作用。为此，在目前的项目中，PI将开发新的计算方法来揭示特定生物体中基因的调节机制和相互作用。特别是，重点是开发新的机器学习方法，这些方法能够预测识别基因组的各种调控元件，使用基因组的良好特征方面作为训练数据。这项研究的预期影响是双重的：它将产生新的方法和软件，分子生物学家可以应用这些方法和软件来深入了解细胞的调节机制；它将通过开发涉及(i)多个相互关联的学习任务的问题域的新方法，（ii）丰富多样的数据来源，包括序列和文本数据，以及（iii）对丰富的表示语言的需求，例如随机上下文无关语法和关系规则，来推进机器学习的最新技术。