A novel features ranking metric with application to scalable visual and bioinformatics data classification

Various microRNAs have been demonstrated to play roles in a number of human diseases. Several microRNA-disease network reconstruction methods have been used to describe the association from a systems biology perspective. The key problem for the network is the similarity computation model. In this article, we reviewed the main similarity computation methods and discussed these methods and future works. This survey may prompt and guide systems biology and bioinformatics researchers to build more perfect microRNA-disease associations and may make the network relationship clear for medical researchers.

Similarity computation strategies in the microRNA-disease network: a survey

Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information

Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier.

Prediction of human protein subcellular localization using deep learning

The analysis of biological information from protein sequences is important for the study of cellular functions and interactions, and protein fold recognition plays a key role in the prediction of protein structures. Unfortunately, the prediction of protein fold patterns is challenging due to the existence of compound protein structures. Here, we processed the latest release of the Structural Classification of Proteins (SCOP, version 1.75) database and exploited novel techniques to impressively increase the accuracy of protein fold classification. The techniques proposed in this paper include ensemble classifying and a hierarchical framework, in the first layer of which similar or redundant sequences were deleted in two manners; a set of base classifiers, fused by various selection strategies, divides the input into seven classes; in the second layer of which, an analogous ensemble method is adopted to predict all protein folds. To our knowledge, it is the first time all protein folds can be intelligently detected hierarchically. Compared with prior studies, our experimental results demonstrated the efficiency and effectiveness of our proposed method, which achieved a success rate of 74.21%, which is much higher than results obtained with previous methods (ranging from 45.6% to 70.5%). When applied to the second layer of classification, the prediction accuracy was in the range between 23.13% and 46.05%. This value, which may not be remarkably high, is scientifically admirable and encouraging as compared to the relatively low counts of proteins from most fold recognition programs. The web server Hierarchical Protein Fold Prediction (HPFP) is available at http://datamining.xmu.edu.cn/software/hpfp.

/pdf/hierarchical-classification-of-protein-folds-using-a-novel-q7jsfn3vys.pdf

Hierarchical classification of protein folds using a novel ensemble classifier.

BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types

Natural Science Foundation of China [61001013, 61102136, 60971085]; Natural Science Foundation of Fujian Province of China [2011J05158, 2010J01350]; Base Research Project of Shenzhen Bureau of Science, Technology, and Information [JC201006030858A]

Yi Jiang

Papers

Hierarchical classification of protein folds using a novel ensemble classifier.

BinMemPredict: a Web Server and Software for Predicting Membrane Protein Types

Identifying Multi-Functional Enzyme by Hierarchical Multi-Label Classifier

Review of Protein Subcellular Localization Prediction

Computational Analysis of miRNA Target Identification