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: 8558105
• 负责人: Willy Wilbur
• 金额: $ 56.4万
• 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/8558105
• 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)许多不同的方法已被调查的目的，聚类集的文件，希望提高检索。不幸的是，这些通常不能提供改进的检索能力。部分问题显然是，一份文件往往涉及一个以上的主题，因此不可能将文件明确归类为明确的类别，而排除其他类别。为了克服这个问题，我们已经开发出的方法，旨在确定一组文件中的主题。主题不必涵盖任何文件的全部。它只需要存在于文档的某个子集中即可识别。其中一些文件可能参与若干主题的界定。发现主题的一种方法是基于EM算法，并且需要收敛到主题的迭代过程。该方法已被实施和测试，并发现是成功的。 2)第二种方法可以基于奇异值分解，并且本质上是向量方法。 3)我们还在研究其他方法来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，并根据这种学习对文档进行评分。然后，PAV可以被应用到所得到的分数，并且该分数函数可以被离散化而不会丢失重要信息。这允许我们将结果用作可以在另一个分类器中单独加权的特征。 4)我们已经开发了一种新的算法称为周期性随机轨道算法（PROBE），这是适用于最小化任何凸损失函数。我们已经将其应用于MeSH分类问题，它似乎工作得很好，比这样一个大问题的替代品更好。