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: 8344948
• 负责人: Willy Wilbur
• 金额: $ 59.96万
• 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/8344948
• 关键词: Algorithms; Bioinformatics; Categories; Classification; Data Set; Exclusion; Learning; Literature; Machine Learning; MeSH Thesaurus; Methods; Procedures; Retrieval; Testing; Weight; Work; base; design; improved; loss of function; vector

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 we are currently studying is to perform machine learning with an SVM or other classifier and score the documents based on this learning. Then PAV can be applied to the resulting scores and this score function can be descretized without the loss of significant information. This allows us to make use of the results as features which can be individually weighted in another classifier. 4) We have developed a new algorithm called the periodic random orbiter algorithm (PROBE) which is applicable to minimize any convex loss function. We have applied it to the MeSH classification problem and it seems to work very well and better than the alternatives on such a large problem.

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。我们目前正在研究的一种方法是使用SVM或其他分类器进行机器学习，并根据此学习对文档进行评分。然后可以将PAV应用于所得的分数，并且可以将此分数函数描述而不会丢失重要信息。这使我们可以将结果用作可以在另一个分类器中单独加权的功能。 4）我们开发了一种称为周期性随机轨道算法（探针）的新算法，该算法适用于最小化任何凸损耗函数。我们将其应用于网格分类问题，似乎比在如此大的问题上的替代方案更好，更好。