Rangwala Huzefa, Karypis George
Department of Computer Science and Engineering, University of Minnesota Minneapolis, MN 55455, USA.
Bioinformatics. 2005 Dec 1;21(23):4239-47. doi: 10.1093/bioinformatics/bti687. Epub 2005 Sep 27.
Protein remote homology detection is a central problem in computational biology. Supervised learning algorithms based on support vector machines are currently one of the most effective methods for remote homology detection. The performance of these methods depends on how the protein sequences are modeled and on the method used to compute the kernel function between them.
We introduce two classes of kernel functions that are constructed by combining sequence profiles with new and existing approaches for determining the similarity between pairs of protein sequences. These kernels are constructed directly from these explicit protein similarity measures and employ effective profile-to-profile scoring schemes for measuring the similarity between pairs of proteins. Experiments with remote homology detection and fold recognition problems show that these kernels are capable of producing results that are substantially better than those produced by all of the existing state-of-the-art SVM-based methods. In addition, the experiments show that these kernels, even when used in the absence of profiles, produce results that are better than those produced by existing non-profile-based schemes.
The programs for computing the various kernel functions are available on request from the authors.
蛋白质远程同源性检测是计算生物学中的核心问题。基于支持向量机的监督学习算法是目前进行远程同源性检测最有效的方法之一。这些方法的性能取决于蛋白质序列的建模方式以及用于计算它们之间核函数的方法。
我们引入了两类核函数，它们是通过将序列谱与用于确定蛋白质序列对之间相似性的新方法和现有方法相结合而构建的。这些核函数直接从这些明确的蛋白质相似性度量构建而成，并采用有效的谱对谱评分方案来测量蛋白质对之间的相似性。对远程同源性检测和折叠识别问题的实验表明，这些核函数能够产生比所有现有的基于支持向量机的先进方法所产生的结果显著更好的结果。此外，实验表明，这些核函数即使在没有谱的情况下使用，产生的结果也比现有的基于非谱的方案更好。
可应作者要求提供计算各种核函数的程序。
