Successive overrelaxation for laplacian support vector machine

Prediction of microRNA-binding residues in protein using a Laplacian support vector machine based on sequence information

The identification of microRNA (miRNA)-binding protein residues significantly impacts several research areas, including gene regulation and expression. We propose a method, PmiRBR, which combines a novel hybrid feature with the Laplacian support vector machine (LapSVM) algorithm to predict miRNA-binding residues in protein sequences. The hybrid feature is constituted by secondary structure, conservation scores, and a novel feature, which includes evolutionary information combined with the physicochemical properties of amino acids. Performance comparisons of the various features indicate that our novel feature contributes the most to prediction improvement. Our results demonstrate that PmiRBR can achieve 85.96% overall accuracy, with 43.89% sensitivity and 90.56% specificity. PmiRBR significantly outperforms other approaches at miRNA-binding residue prediction.

SCE: A Manifold Regularized Set-Covering Method for Data Partitioning

Cluster analysis plays a very important role in data analysis. In these years, cluster ensemble, as a cluster analysis tool, has drawn much attention for its robustness, stability, and accuracy. Many efforts have been done to combine different initial clustering results into a single clustering solution with better performance. However, they neglect the structure information of the raw data in performing the cluster ensemble. In this paper, we propose a Structural Cluster Ensemble (SCE) algorithm for data partitioning formulated as a set-covering problem. In particular, we construct a Laplacian regularized objective function to capture the structure information among clusters. Moreover, considering the importance of the discriminative information underlying in the initial clustering results, we add a discriminative constraint into our proposed objective function. Finally, we verify the performance of the SCE algorithm on both synthetic and real data sets. The experimental results show the effectiveness of our proposed method SCE algorithm.