DETECTING REMOTE PROTEIN EVOLUTIONARY AND STRUCTURAL RELATIONSHIPS VIA STRING SCORING METHOD
The amount of information being churned out by the field of biology has jumped manifold and now requires the extensive use of computer techniques for the management of this information. In this work, we propose, an effective learning method for detecting remote protein homology. The proposed method uses a transformation that converts protein domains into fixed-dimensional representative feature vectors,where each feature records the sensitivity of a set of substrings to a previously learned protein domain. These features are then used to compute the kernel matrix that will be used in conjunction with support vector machines. The proposed method is tested and evaluated on two different benchmark protein datasets and its able to deliver remarkable improvements over most of the existing homology detection methods.
Protein homology detection support vector machine string kernel
NAZAR ZAKI
College of Information Technology, UAE University, Al Ain 17555, UAE
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
4300-4305
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）