Detailed knowledge of interfacial region between interacting proteins is not only helpful in annotating function for proteins, but also very important for structure-based drug design and disease treatment. However, th...Detailed knowledge of interfacial region between interacting proteins is not only helpful in annotating function for proteins, but also very important for structure-based drug design and disease treatment. However, this is one of the most difficult tasks and current methods are constrained by some factors. In this study, we developed a new method to predict residue-residue contacts of two interacting protein domains by integrating information about evolutionary couplings andamino acid pairwise contact potentials, as well as domain-domain interaction interfaces. The experimental results showed that our proposed method outperformed the previous method with the same datasets. Moreover, the method promises an improvement in the source of template-based protein docking.展开更多
MicroRNAs (miRNAs) are short (~22nt) non-coding RNAs that play an indispensable role in gene regulation of many biological processes. Most of current computational, comparative, and non-comparative methods commonly cl...MicroRNAs (miRNAs) are short (~22nt) non-coding RNAs that play an indispensable role in gene regulation of many biological processes. Most of current computational, comparative, and non-comparative methods commonly classify human precursor micro- RNA (pre-miRNA) hairpins from both genome pseudo hairpins and other non-coding RNAs (ncRNAs). Although there were a few approaches achieving promising results in applying class imbalance learning methods, this issue has still not solved completely and successfully yet by the existing methods because of imbalanced class distribution in the datasets. For example, SMOTE is a famous and general over-sampling method addressing this problem, however in some cases it cannot improve or sometimes reduces classification performance. Therefore, we developed a novel over-sampling method named incre-mental- SMOTE to distinguish human pre-miRNA hairpins from both genome pseudo hairpins and other ncRNAs. Experimental results on pre-miRNA datasets from Batuwita et al. showed that our method achieved better Sensitivity and G-mean than the control (no over- sampling), SMOTE, and several successsors of modified SMOTE including safe-level-SMOTE and border-line-SMOTE. In addition, we also applied the novel method to five imbalanced benchmark datasets from UCI Machine Learning Repository and achieved improvements in Sensitivity and G-mean. These results suggest that our method outperforms SMOTE and several successors of it in various biomedical classification problems including miRNA classification.展开更多
β-turn is one of the most important reverse turns because of its role in protein folding. Many computational methods have been studied for predicting β-turns and β-turn types. However, due to the imbalanced dataset...β-turn is one of the most important reverse turns because of its role in protein folding. Many computational methods have been studied for predicting β-turns and β-turn types. However, due to the imbalanced dataset, the performance is still inadequate. In this study, we proposed a novel over-sampling technique FOST to deal with the class-imbalance problem. Experimental results on three standard benchmark datasets showed that our method is comparable with state-of-the-art methods. In addition, we applied our algorithm to five benchmark datasets from UCI Machine Learning Repository and achieved significant improvement in G-mean and Sensitivity. It means that our method is also effective for various imbalanced data other than β-turns and β-turn types.展开更多
In this study, we propose a data preprocessing algorithm called D-IMPACT inspired by the IMPACT clustering algorithm. D-IMPACT iteratively moves data points based on attraction and density to detect and remove noise a...In this study, we propose a data preprocessing algorithm called D-IMPACT inspired by the IMPACT clustering algorithm. D-IMPACT iteratively moves data points based on attraction and density to detect and remove noise and outliers, and separate clusters. Our experimental results on two-dimensional datasets and practical datasets show that this algorithm can produce new datasets such that the performance of the clustering algorithm is improved.展开更多
文摘Detailed knowledge of interfacial region between interacting proteins is not only helpful in annotating function for proteins, but also very important for structure-based drug design and disease treatment. However, this is one of the most difficult tasks and current methods are constrained by some factors. In this study, we developed a new method to predict residue-residue contacts of two interacting protein domains by integrating information about evolutionary couplings andamino acid pairwise contact potentials, as well as domain-domain interaction interfaces. The experimental results showed that our proposed method outperformed the previous method with the same datasets. Moreover, the method promises an improvement in the source of template-based protein docking.
文摘MicroRNAs (miRNAs) are short (~22nt) non-coding RNAs that play an indispensable role in gene regulation of many biological processes. Most of current computational, comparative, and non-comparative methods commonly classify human precursor micro- RNA (pre-miRNA) hairpins from both genome pseudo hairpins and other non-coding RNAs (ncRNAs). Although there were a few approaches achieving promising results in applying class imbalance learning methods, this issue has still not solved completely and successfully yet by the existing methods because of imbalanced class distribution in the datasets. For example, SMOTE is a famous and general over-sampling method addressing this problem, however in some cases it cannot improve or sometimes reduces classification performance. Therefore, we developed a novel over-sampling method named incre-mental- SMOTE to distinguish human pre-miRNA hairpins from both genome pseudo hairpins and other ncRNAs. Experimental results on pre-miRNA datasets from Batuwita et al. showed that our method achieved better Sensitivity and G-mean than the control (no over- sampling), SMOTE, and several successsors of modified SMOTE including safe-level-SMOTE and border-line-SMOTE. In addition, we also applied the novel method to five imbalanced benchmark datasets from UCI Machine Learning Repository and achieved improvements in Sensitivity and G-mean. These results suggest that our method outperforms SMOTE and several successors of it in various biomedical classification problems including miRNA classification.
文摘β-turn is one of the most important reverse turns because of its role in protein folding. Many computational methods have been studied for predicting β-turns and β-turn types. However, due to the imbalanced dataset, the performance is still inadequate. In this study, we proposed a novel over-sampling technique FOST to deal with the class-imbalance problem. Experimental results on three standard benchmark datasets showed that our method is comparable with state-of-the-art methods. In addition, we applied our algorithm to five benchmark datasets from UCI Machine Learning Repository and achieved significant improvement in G-mean and Sensitivity. It means that our method is also effective for various imbalanced data other than β-turns and β-turn types.
文摘In this study, we propose a data preprocessing algorithm called D-IMPACT inspired by the IMPACT clustering algorithm. D-IMPACT iteratively moves data points based on attraction and density to detect and remove noise and outliers, and separate clusters. Our experimental results on two-dimensional datasets and practical datasets show that this algorithm can produce new datasets such that the performance of the clustering algorithm is improved.
