General and Semi-supervised Machine Learning Applied to Bioinformatics

应用于生物信息学的通用和半监督机器学习

• 批准号: 8149602
• 负责人: Willy Wilbur
• 金额: $ 47.01万
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8149602
• 关键词: Bioinformatics; Machine Learning

1) Many different methods have been investigated for the purpose of clustering sets of documents with the hope of improving retrieval. Unfortunately these have generally failed to provide improved retrieval capability. Part of the problem is clearly the fact that a given document often involves more than one subject so that it is not possible to make a clean categorization of the documents into definite categories to the exclusion of others. In order to overcome this problem we have developed methods that are designed to identify a theme among a set of documents. The theme need not encompass the whole of any document. It only needs to exist in some subset of the documents in order to be identifiable. Some of these same documents may participate in the definition of several themes. One method of finding themes is based on the EM algorithm and requires an iterative procedure which converges to themes. The method has been implemented and tested and found to be successful. 2) A second approach can be based on the singular value decomposition and essentially is a vector approach. 3) We are also investigating other methods to extract higher level features. One method of interest is the method known as sparse coding, which is the basis of self-taught learning.

1）已经对许多不同的方法进行了研究，目的是将文档集群集，希望改善检索。不幸的是，这些通常无法提供提高的检索能力。问题的一部分显然是一个事实，即给定的文档通常涉及多个主题，因此不可能将文档分类为确定的类别以排除其他主题。为了克服这个问题，我们开发了旨在确定一组文档中主题的方法。该主题不必涵盖所有文档的全部。它只需要在文档的某些子集中存在才能识别。这些相同的文档中的一些可能参与了几个主题的定义。查找主题的一种方法是基于EM算法，需要一个迭代过程，该过程会收敛到主题。该方法已被实施和测试，并发现成功。 2）第二种方法可以基于单数值分解，而本质上是矢量方法。 3）我们还正在研究其他方法以提取更高级别的特征。一种感兴趣的方法是称为稀疏编码的方法，这是自学学习的基础。