DocumentCode :
2900025
Title :
Detecting Remote Protein Evolutionary and Structural Relationships via String Scoring Method
Author :
Zaki, Nazar
Author_Institution :
Coll. of Inf. Technol., United Arab Emirates Univ., Al-Ain
fYear :
2006
fDate :
13-16 Aug. 2006
Firstpage :
4300
Lastpage :
4305
Abstract :
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 it´s able to deliver remarkable improvements over most of the existing homology detection methods
Keywords :
biology computing; evolution (biological); feature extraction; learning (artificial intelligence); proteins; string matching; support vector machines; fixed-dimensional representative feature vector; kernel matrix computing; learning method; remote protein evolutionary relationship detection; remote protein homology detection; remote protein structural relationship detection; string scoring method; support vector machine; Biological information theory; Biology computing; Cybernetics; Educational institutions; Hidden Markov models; Information technology; Iterative methods; Kernel; Machine learning; Matrix converters; Protein sequence; Support vector machines; Protein homology detection; string kernel; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
Type :
conf
DOI :
10.1109/ICMLC.2006.259017
Filename :
4028829
Link To Document :
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