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Protein structural class prediction can play a vital role in protein 3-D structure prediction by reducing the search space of 3-D structure prediction algorithms. In this paper we used support vector machine to predict protein structural class solely based of its amino acid sequences, i.e. mainly α, mainly β, α- β and fss from CATH protein structure database; all-α, all-β, α/β, α+β from SCOP protein...
Disulfide bonds play the key role for predicting the three-dimensional structure and the function of a protein. In this paper, we propose an algorithm for predicting the disulfide bonding state of each cysteine in a protein sequence. This method is based on the multi-stage framework and the multi-classifier of the support vector machine. We also design a new training strategy to increase the prediction...
The problem of fusing indefinite similarity information and positive semidefinite similarity information together for classification is considered. The proposed solution jointly (i) learns a spectrum modification to make the indefinite similarity positive semidefinite, (ii) learns a conic combination of multiple given positive semidefinite kernels, and (iii) learns the parameters of a discriminative...
Reducing the dimension of vectors used in training support vector machines (SVMs) results in a proportional speedup in training time. For large-scale problems this can make the difference between tractable and intractable training tasks. However, it is critical that classifiers trained on reduced datasets perform as reliably as their counterparts trained on high-dimensional data. We assessed principal...
The performance of support vector machines (SVM) drops significantly while facing imbalanced datasets, though it has been extensively studied and has shown remarkable success in many applications. Some researchers have pointed out that it is difficult to avoid such decrease when trying to improve the efficient of SVM on imbalanced datasets by modifying the algorithm itself only. Therefore, as the...
In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning with SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of...
A new simple method for identification of GPI-(like)-anchored proteins at sequence level is proposed in this paper. As a binary classifier of GPI-(like)-anchored proteins and non GPI-(like)-anchored proteins, a supervised machine learning algorithm, support vector machine (SVM) and simple representation of C-terminus of protein primary sequences with mean hydrophobicity were used. Not merely does...
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