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The one class classification (OCC), a special learning framework dealing with the positive and unlabeled dataset, has ubiquitous applications, especially in the era of information-explosion. Since it has aroused researchers' interests from both academia and industry, we provide a comprehensive revisit to algorithms for the OCC from the perspective of methodologies with the ideas of essence behind...
AdaBoost incorporating properly designed RBFSVM is a popular boosting method and demonstrates better generalization performance than SVM on imbalanced classification problems. This paper discusses the application of AdaBoostSVM algorithms to the problem of G-protein-coupled receptor classes prediction in which the pseudo amino acid composition is derived by combining "the cellular automaton image"...
By using of the composite vector with increment of diversity and scoring function to express the information of sequence, a support vector machine (SVM) algorithm for predicting the eight types of membrane proteins is proposed. The overall jackknife success rate is 91.81% what is higher than other results. In order to evaluate the predictive method, the six types of membrane proteins are predicted...
This paper presents a novel method to extract Protein-Protein Interaction (PPI) information from biomedical literatures based on Support Vector Machine (SVM) and K Nearest Neighbors (KNN). The two protein names, words between two proteins, words surrounding two proteins, keyword between or among the surrounding words of two protein names, ExpDistance based on word distance of two proteins, ProDistance...
At first, this paper reviews the development history of the protein secondary structure prediction. Some concerned secondary structure prediction methods are introduced. Then a novel method is proposed, which substantially improves the prediction accuracy of CB513 with 80.49% and RS126 with 82.79% respectively. In the end, this paper points out several possible trends in the protein secondary structure...
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...
Proteins function through interactions with other proteins, compounds, RNA and DNA. Prediction of protein interface sites is the key process for providing clues to the function of a protein, and is becoming increasing relevant to drug discovery. In this paper, combining the protein features with the theory of granular computing of quotient space based on protein-protein interaction sites classification...
Human mitochondrial proteins are involved in fundamental biological process including apoptosis, energy production and many metabolic pathways, prediction of mitochondrial proteins is a major challenge in genome annotation. In this study, we implemented a machine learning approach and developed reliable neural network and SVM based methods to classify human mitochondria proteins with high confidence...
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...
We present a grammatical swarm (GS) for the optimization of an aggregation operator. This combines the results of several classifiers into a unique score, producing an optimal ranking of the individuals. We apply our method to the identification of new members of a protein family. Support vector machine and naive Bayes classifiers exploit complementary features to compute probability estimates. A...
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