PREDICTION OF PROTEIN SUBCELLULAR LOCALIZATION WITH A NOVEL METHOD: SEQUENCE-SEGMENTED PSEAAC
Information of the subcelluiar localizations of proteins is important because it can provide useful insights about their functions, as well as how and in what kind of cellular environments they interact with each other and with other molecules. Facing the explosion of newly generated protein sequences In the port gcnomic era, we are challenged to develop an automated method for fast and reliably annotating their subcelluiar localizations. To tackle the challenge, a novel method of the sequence-segmented pseudo amino acid composition (PscAAC) is introduced to represent protein samples. Based on the concept of Chous PseAAC, a series of useful information and techniques, such as multi-scale energy and moment descriptors were utilized to generate the sequence-segmented pseudo amino acid components for representing the protein samples. Meanwhile, the multi-class SVM classifier modules were adopted for predicting 16 kinds of eukaryotic protein subcelluiar localizations. Compared with existing methods, this new approach provides better predictive performance The success total accuracies were obtained in the jackknife test and independent dataset test, suggesting that the sequence-segmented PscAAC method is quite promising, and might also hold a great potential as a? useful vehicle for the other areas of molecular biology.
sequence-segmented PseAAC multi-scale energy moment descriptor support vector machine
SHAO-WU ZHANG HUI-FANG YANG QI-PENG LI YONG-MEI CHENG QUAN PAN
College of Automation, Northwestern Ploytechnical University, 710072, Xian, China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
4024-4028
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）