Boosting classifier for predicting protein domain structural class

Kong, Feng; Cai, Yu-Dong; Chou, Kuo‐Chen

doi:10.1016/j.bbrc.2005.06.075

Cited by 123 publications

(54 citation statements)

References 54 publications

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“…These algorithms include neural network (Cai and Zhou 2000), support vector machine (SVM) (Cai et al 2001;Chen et al 2006a;Li et al 2008;Qiu et al 2009), fuzzy k-nearest neighbor (Zhang et al 2008, fuzzy clustering (Shen et al 2005), Bayesian classification (Wang and Yuan 2000), logistic regression (Kurgan and Chen 2007;Kurgan and Homaeian 2006), rough sets (Cao et al 2006) and classifier fusion techniques (Cai et al 2006;Chen et al 2006bChen et al , 2009Feng et al 2005;Kedarisetti et al 2006). Among them, SVM is the most popular and the best-performing classifier for this task (Kurgan et al 2008a).…”

Section: Introductionmentioning

confidence: 98%

Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles

et al. 2011

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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.

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Section: Introductionmentioning

confidence: 98%

Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles

et al. 2011

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“…SVM is a new paradigm of learning system. The technique of SVM, developed by Vapnik [15], is a powerful widely used technique for solving supervised biological classification problems due to its generalization ability [16][17][18]. In essence, SVM classifiers maximize the margin between training data and the decision boundary (optimal separating hyperplane), which can be formulated as a quadratic optimization problem in a feature space.…”

Section: Support Vector Machinementioning

confidence: 99%

Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition

Shu

2008

Neural Comput & Applic

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The human heart is a complex system that reveals many clues about its condition in its electrocardiogram (ECG) signal, and ECG supervising is the most important and efficient way of preventing heart attacks. ECG analysis and recognition are both important and tempting topics in modern medical research. The purpose of this paper is to develop an algorithm which investigates kernel method, locally linear embedding (LLE), principal component analysis (PCA), and support vector machine(SVM) algorithms for dimensionality reduction, features extraction, and classification for recognizing and classifying the given ECG signals. In order to do so, a nonlinear dimensionality reduction kernel method based LLE is proposed to reduce the high dimensions of the variational ECG signals, and the principal characteristics of the signals are extracted from the original database by means of the PCA, each signal representing a single and complete heart beat. SVM method is applied to classify the ECG data into several categories of heart diseases. Experimental results obtained demonstrated that the performance of the proposed method was similar and sometimes better when compared to other ECG recognition techniques, thus indicating a viable and accurate technique.

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“…[13] Recently, predicted secondary structural sequences (PSSF) were successfully used in the prediction of protein structural class. [14][15][16][17] In addition to the representations of protein information, various classification algorithms have already been introduced to the structural class prediction methods, including component coupled algorithm, [9] support vector machine (SVM), [18] rough sets, [19] and LogitBoost, [20] and more details on structural class prediction methods were reviewed by Chou.…”

Section: Introductionmentioning

confidence: 99%

Improving protein structural class prediction using novel combined sequence information and predicted secondary structural features

Dai¹,

Wu²,

Li³

2011

J Comput Chem

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Protein structural class prediction solely from protein sequences is a challenging problem in bioinformatics. Numerous efficient methods have been proposed for protein structural class prediction, but challenges remain. Using novel combined sequence information coupled with predicted secondary structural features (PSSF), we proposed a novel scheme to improve prediction of protein structural classes. Given an amino acid sequence, we first transformed it into a reduced amino acid sequence and calculated its word frequencies and word position features to combine novel sequence information. Then we added the PSSF to the combine sequence information to predict protein structural classes. The proposed method was tested on four benchmark datasets in low homology and achieved the overall prediction accuracies of 83.1%, 87.0%, 94.5%, and 85.2%, respectively. The comparison with existing methods demonstrates that the overall improvements range from 2.3% to 27.5%, which indicates that the proposed method is more efficient, especially for low-homology amino acid sequences.

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Boosting classifier for predicting protein domain structural class

Cited by 123 publications

References 54 publications

Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles

Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles

Kernel-based nonlinear dimensionality reduction for electrocardiogram recognition

Improving protein structural class prediction using novel combined sequence information and predicted secondary structural features

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