蛋白质功能族随机树的模式识别
Pattern recognition on random trees associated to protein functionality families
摘要：
在本文中，我们解决的问题，确定蛋白质的功能，使用其氨基酸序列中所包含的信息。我们提出了一种方法来定义序列相似性关系，可以作为输入的分类和聚类通过众所周知的度量为基础的统计方法。在我们的例子中，我们通过树空间上的基于简单度量的技术具体解决了结构基因组学中的监督和无监督学习的两个问题1）通过树空间中的K均值聚类对功能家族进行无监督检测，2）通过k最近邻树将新蛋白质分类到已知家族中。我们发现的证据表明，我们的方法引起的相似性度量集中的歧视信息。分类具有与其他VLMC方法相同的高性能。聚类是一项更困难的任务，但是，我们的聚类方法是无对齐和自动的，并且可以通过选择基于预先计算的相似性信息的其他聚类或分类程序来产生许多有趣的变化，如使用流动模拟执行聚类的程序，参见（Yona等人2000，Enright等人，2003）。
英文摘要：
In this paper, we address the problem of identifying protein functionality using the information contained in its aminoacid sequence. We propose a method to define sequence similarity relationships that can be used as input for classification and clustering via well known metric based statistical methods. In our examples, we specifically address two problems of supervised and unsupervised learning in structural genomics via simple metric based techniques on the space of trees 1)Unsupervised detection of functionality families via K means clustering in the space of trees, 2)Classification of new proteins into known families via k nearest neighbour trees. We found evidence that the similarity measure induced by our approach concentrates information for discrimination. Classification has the same high performance than others VLMC approaches. Clustering is a harder task, though, but our approach for clustering is alignment free and automatic, and may lead to many interesting variations by choosing other clustering or classification procedures that are based on pre-computed similarity information, as the ones that performs clustering using flow simulation, see (Yona et al 2000, Enright et al, 2003).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
