In this short review paper, the significant and profound impacts of the Pseudo Amino Acid Composition or PseAAC have been briefly presented with crystal clear convincingness.
Membrane proteins are embedded in the lipid bilayer,which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological function ...Membrane proteins are embedded in the lipid bilayer,which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological function and its interaction process with other molecules in a biological system. Membrane proteins have different types. The function of a membrane protein is closely correlated with the type it belongs to. In this study,on the basis of the concept of pseudo amino acid (PseAA) composition originally introduced by Chou,the value of approximate entropy (ApEn) of the query membrane protein was used to integrate the complementary information. By fusing fifteen powerful individual fuzzy K-nearest neighbor ( FKNN) classifiers,an ensemble classifier was presented. Each basic classifier was trained in PseAA composition of membrane protein sequences with different parameters. The results of experiments demonstrate it is efficient for the structural prediction of membrane proteins.展开更多
Protein remote homology detection is a key problem in bioinformatics. Currently, the discriminative methods, such as Support Vector Machine (SVM), can achieve the best performance. The most efficient approach to impro...Protein remote homology detection is a key problem in bioinformatics. Currently, the discriminative methods, such as Support Vector Machine (SVM), can achieve the best performance. The most efficient approach to improve the performance of the SVM-based methods is to find a general protein representation method that is able to convert proteins with different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination. The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we applied this approach for protein remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated into the PseAAC to improve the generalization ability of this method. Our experiments on a well-known benchmark show this method achieves superior or comparable performance with current state-of-the-art methods.展开更多
文摘In this short review paper, the significant and profound impacts of the Pseudo Amino Acid Composition or PseAAC have been briefly presented with crystal clear convincingness.
基金National Nature Science Foundations of China (No.60975059, No.60775052)Specialized Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China ( No.20090075110002)Projects of the Shanghai Committee of Science and Technology (No.09JC1400900, No.08JC1400100, No.10DZ0506500)
文摘Membrane proteins are embedded in the lipid bilayer,which creates a suitable environment for their actions. It is important to decide which tpye it belongs to because it is closely relevant to its biological function and its interaction process with other molecules in a biological system. Membrane proteins have different types. The function of a membrane protein is closely correlated with the type it belongs to. In this study,on the basis of the concept of pseudo amino acid (PseAA) composition originally introduced by Chou,the value of approximate entropy (ApEn) of the query membrane protein was used to integrate the complementary information. By fusing fifteen powerful individual fuzzy K-nearest neighbor ( FKNN) classifiers,an ensemble classifier was presented. Each basic classifier was trained in PseAA composition of membrane protein sequences with different parameters. The results of experiments demonstrate it is efficient for the structural prediction of membrane proteins.
文摘Protein remote homology detection is a key problem in bioinformatics. Currently, the discriminative methods, such as Support Vector Machine (SVM), can achieve the best performance. The most efficient approach to improve the performance of the SVM-based methods is to find a general protein representation method that is able to convert proteins with different lengths into fixed length vectors and captures the different properties of the proteins for the discrimination. The bottleneck of designing the protein representation method is that native proteins have different lengths. Motivated by the success of the pseudo amino acid composition (PseAAC) proposed by Chou, we applied this approach for protein remote homology detection. Some new indices derived from the amino acid index (AAIndex) database are incorporated into the PseAAC to improve the generalization ability of this method. Our experiments on a well-known benchmark show this method achieves superior or comparable performance with current state-of-the-art methods.
