The Experts below are selected from a list of 25272 Experts worldwide ranked by ideXlab platform
Xiaoqi Zheng - One of the best experts on this subject based on the ideXlab platform.
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Zheng X: PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations. PLoS One 2014
2016Co-Authors: Xiang Cui, Yuan Zhang, Yue Zhou, Hua Yang, Zhong Luo, Xiaoqi ZhengAbstract:Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40 % in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific Score Matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking Score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracie
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Prediction of bacterial protein subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach.
Biochimie, 2014Co-Authors: Weidong Xiao, Xiaoqi Zheng, Lan Huang, Shiwen Zhou, Hua YangAbstract:Information on the subcellular localization of bacterial proteins is essential for protein function prediction, genome annotation and drug design. Here we proposed a novel approach to predict the subcellular localization of bacterial proteins by fusing features from position-specific Score Matrix (PSSM), Gene Ontology (GO) and PROFEAT. A backward feature selection approach by linear kennel of SVM was then used to rank the integrated feature vectors and extract optimal features. Finally, SVM was applied for predicting protein subcellular locations based on these optimal features. To validate the performance of our method, we employed jackknife cross-validation tests on three low similarity datasets, i.e., M638, Gneg1456 and Gpos523. The overall accuracies of 94.98%, 93.21%, and 94.57% were achieved for these three datasets, which are higher (from 1.8% to 10.9%) than those by state-of-the-art tools. Comparison results suggest that our method could serve as a very useful vehicle for expediting the prediction of bacterial protein subcellular localization.
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pssp rfe accurate prediction of protein structural class by recursive feature extraction from psi blast profile physical chemical property and functional annotations
PLOS ONE, 2014Co-Authors: Liqi Li, Sanjiu Yu, Xiaoqi Zheng, Yuan Zhang, Yue Zhou, Hua YangAbstract:Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific Score Matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking Score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.
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accurate prediction of protein structural class using auto covariance transformation of psi blast profiles
Amino Acids, 2012Co-Authors: Taigang Liu, Xiaoqi Zheng, Xingbo Geng, Jun WangAbstract:Computational prediction of protein structural class based solely on sequence data remains a challenging problem in protein science. Existing methods differ in the protein sequence representation models and prediction engines adopted. In this study, a powerful feature extraction method, which combines position-specific Score Matrix (PSSM) with auto covariance (AC) transformation, is introduced. Thus, a sample protein is represented by a series of discrete components, which could partially incorporate the long-range sequence order information and evolutionary information reflected from the PSI-BLAST profile. To verify the performance of our method, jackknife cross-validation tests are performed on four widely used benchmark datasets. Comparison of our results with existing methods shows that our method provides the state-of-the-art performance for structural class prediction. A Web server that implements the proposed method is freely available at http://202.194.133.5/xinxi/AAC_PSSM_AC/index.htm.
Hua Yang - One of the best experts on this subject based on the ideXlab platform.
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Zheng X: PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations. PLoS One 2014
2016Co-Authors: Xiang Cui, Yuan Zhang, Yue Zhou, Hua Yang, Zhong Luo, Xiaoqi ZhengAbstract:Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40 % in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific Score Matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking Score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracie
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Prediction of bacterial protein subcellular localization by incorporating various features into Chou's PseAAC and a backward feature selection approach.
Biochimie, 2014Co-Authors: Weidong Xiao, Xiaoqi Zheng, Lan Huang, Shiwen Zhou, Hua YangAbstract:Information on the subcellular localization of bacterial proteins is essential for protein function prediction, genome annotation and drug design. Here we proposed a novel approach to predict the subcellular localization of bacterial proteins by fusing features from position-specific Score Matrix (PSSM), Gene Ontology (GO) and PROFEAT. A backward feature selection approach by linear kennel of SVM was then used to rank the integrated feature vectors and extract optimal features. Finally, SVM was applied for predicting protein subcellular locations based on these optimal features. To validate the performance of our method, we employed jackknife cross-validation tests on three low similarity datasets, i.e., M638, Gneg1456 and Gpos523. The overall accuracies of 94.98%, 93.21%, and 94.57% were achieved for these three datasets, which are higher (from 1.8% to 10.9%) than those by state-of-the-art tools. Comparison results suggest that our method could serve as a very useful vehicle for expediting the prediction of bacterial protein subcellular localization.
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pssp rfe accurate prediction of protein structural class by recursive feature extraction from psi blast profile physical chemical property and functional annotations
PLOS ONE, 2014Co-Authors: Liqi Li, Sanjiu Yu, Xiaoqi Zheng, Yuan Zhang, Yue Zhou, Hua YangAbstract:Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific Score Matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking Score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.
David J. Lipman - One of the best experts on this subject based on the ideXlab platform.
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Gapped BLAST and PSI-BLAST: A new generation of protein database search programs
Nucleic Acids Research, 1997Co-Authors: Stephen F Altschul, Webb Miller, Alejandro A Schaffer, Thomas L Madden, Jinghui Zhang, Zheng Zhang, David J. LipmanAbstract:The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific Score Matrix, and searching the database using this Matrix. The resulting Position-Specific Iterated BLAST (PSI-BLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.
Young Chel Kwun - One of the best experts on this subject based on the ideXlab platform.
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Extension of the VIKOR method for group decision making with interval-valued intuitionistic fuzzy information
Fuzzy Optimization and Decision Making, 2011Co-Authors: Jin Han Park, Hyun Ju Cho, Young Chel KwunAbstract:The aim of this paper is to extend the VIKOR method for multiple attribute group decision making in interval-valued intuitionistic fuzzy environment, in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number, and the information about attribute weights is partially known, which is an important research field in decision science and operation research. First, we use the interval-valued intuitionistic fuzzy hybrid geometric operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision Matrix, and then we use the Score function to calculate the Score of each attribute value and construct the Score Matrix of the collective interval-valued intuitionistic fuzzy decision Matrix. From the Score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. We use the different distances to calculate the particular measure of closeness of each alternative to the interval-valued intuitionistic positive-ideal solution. According to values of the particular measure, we rank the alternatives and then select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.
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extension of the topsis method for decision making problems under interval valued intuitionistic fuzzy environment
Applied Mathematical Modelling, 2011Co-Authors: Jin Han Park, Il Young Park, Young Chel KwunAbstract:TOPSIS is one of the well-known methods for multiple attribute decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFNs), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision Matrix, and then we use the Score function to calculate the Score of each attribute value and construct the Score Matrix of the collective interval-valued intuitionistic fuzzy decision Matrix. From the Score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and construct the weighted collective interval-valued intuitionistic fuzzy decision Matrix, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. Based on different distance definitions, we calculate the relative closeness of each alternative to the interval-valued intuitionistic positive-ideal solution and rank the alternatives according to the relative closeness to the interval-valued intuitionistic positive-ideal solution and select the most desirable one(s). Finally, an example is used to illustrate the applicability of the proposed approach.
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correlation coefficient of interval valued intuitionistic fuzzy sets and its application to multiple attribute group decision making problems
Mathematical and Computer Modelling, 2009Co-Authors: Dong Gun Park, Young Chel Kwun, Jin Han Park, Il Young ParkAbstract:In this paper, we investigate the group decision making problems in which all the information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFN), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision Matrix, and then we use the Score function to calculate the Score of each attribute value and construct the Score Matrix of the collective interval-valued intuitionistic fuzzy decision Matrix. From the Score Matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and then we use the obtained attribute weights and the interval-valued intuitionistic fuzzy weighted geometric (IIFWG) operator to fuse the interval-valued intuitionistic fuzzy information in the collective interval-valued intuitionistic fuzzy decision Matrix to get the overall interval-valued intuitionistic fuzzy values of alternatives, and then rank the alternatives according to the correlation coefficients between IVIFNs and select the most desirable one(s). Finally, a numerical example is used to illustrate the applicability of the proposed approach.
Eiichiro Fukusaki - One of the best experts on this subject based on the ideXlab platform.
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method for assessing the statistical significance of mass spectral similarities using basic local alignment search tool statistics
Analytical Chemistry, 2013Co-Authors: Fumio Matsuda, Hiroshi Tsugawa, Eiichiro FukusakiAbstract:A novel method for assessing the statistical significance of mass spectral similarities was developed using modified basic local alignment search tool (BLAST; Karlin–Altschul) statistics. In gas chromatography/mass spectrometry-based metabolomics, many signals in raw metabolome data are identified on the basis of unexpected similarities among mass spectra and the spectra of standards. Since there is inevitably noise in the observed spectra, a list of identified metabolites includes some false positives. In the developed method, electron ionization (EI) mass spectrometry–BLAST, a similarity Score of two mass spectra is calculated using a general scoring scheme, from which the probability of obtaining the Score by chance (P value) is calculated. For this purpose, a simple rule for converting a unit EI mass spectrum to a mass spectral sequence as well as a Score Matrix for aligned mass spectral sequences was developed. A Monte Carlo simulation using randomly generated mass spectral sequences demonstrated tha...
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method for assessing the statistical significance of mass spectral similarities using basic local alignment search tool statistics
Analytical Chemistry, 2013Co-Authors: Fumio Matsuda, Hiroshi Tsugawa, Eiichiro FukusakiAbstract:A novel method for assessing the statistical significance of mass spectral similarities was developed using modified basic local alignment search tool (BLAST; Karlin-Altschul) statistics. In gas chromatography/mass spectrometry-based metabolomics, many signals in raw metabolome data are identified on the basis of unexpected similarities among mass spectra and the spectra of standards. Since there is inevitably noise in the observed spectra, a list of identified metabolites includes some false positives. In the developed method, electron ionization (EI) mass spectrometry-BLAST, a similarity Score of two mass spectra is calculated using a general scoring scheme, from which the probability of obtaining the Score by chance (P value) is calculated. For this purpose, a simple rule for converting a unit EI mass spectrum to a mass spectral sequence as well as a Score Matrix for aligned mass spectral sequences was developed. A Monte Carlo simulation using randomly generated mass spectral sequences demonstrated that the null distribution or the expected number of hits (E value) follows modified Karlin-Altschul statistics. A metabolite data set obtained from green tea extract was analyzed using the developed method. Among 171 metabolite signals in the metabolome data, 93 signals were identified on the basis of significant similarities (P < 0.015) with reference data. Since the expected number of false positives is 2.6, the false discovery rate was estimated to be 2.8%, indicating that the search threshold (P < 0.015) is reasonable for metabolite identification.
