Engineering support vector machine kernels that recognize translation initiation sites
Engineering support vector machine kernels that recognize translation initiation sites
复制标题
DOI：
10.1093/bioinformatics/16.9.799
复制
发表时间：
2000-09-01
期刊：
影响因子：
5.8
通讯作者：
Müller, KR
中科院分区：
文献类型：
--
作者：
Zien, A;Rätsch, G;Müller, KR
动机：为了从核苷酸序列中提取蛋白质序列，识别编码蛋白质的区域的起始点是重要的一步。这些点被称为翻译起始位点（TIS）。结果：寻找TIS的任务可以建模为一个分类问题。我们证明了支持向量机的适用性，这项任务，并展示了如何将事先的生物学知识，工程一个适当的核函数。与所描述的技术的识别性能可以提高26%，超过领先的现有方法。我们提供的证据表明，现有的相关方法（如ESTScan）可以受益于先进的TIS识别。
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS).Results: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.