DNA-binding proteins are vital for the study of cellular processes. In recent genome engineering studies, the identification of proteins with certain functions has become increasingly important and needs to be performed rapidly and efficiently. In previous years, several approaches have been developed to improve the identification of DNA-binding proteins. However, the currently available resources are insufficient to accurately identify these proteins. Because of this, the previous research has been limited by the relatively unbalanced accuracy rate and the low identification success of the current methods. In this paper, we explored the practicality of modelling DNA binding identification and simultaneously employed an ensemble classifier, and a new predictor (nDNA-Prot) was designed. The presented framework is comprised of two stages: a 188-dimension feature extraction method to obtain the protein structure and an ensemble classifier designated as imDC. Experiments using different datasets showed that our method is more successful than the traditional methods in identifying DNA-binding proteins. The identification was conducted using a feature that selected the minimum Redundancy and Maximum Relevance (mRMR). An accuracy rate of 95.80% and an Area Under the Curve (AUC) value of 0.986 were obtained in a cross validation. A test dataset was tested in our method and resulted in an 86% accuracy, versus a 76% using iDNA-Prot and a 68% accuracy using DNA-Prot. Our method can help to accurately identify DNA-binding proteins, and the web server is accessible at http://datamining.xmu.edu.cn/~songli/nDNA . In addition, we also predicted possible DNA-binding protein sequences in all of the sequences from the UniProtKB/Swiss-Prot database.

To assess the gait variability in patients with Parkinson's disease (PD), we first used the nonparametric Parzen-window method to estimate the probability density functions (PDFs) of stride interval and its two subphases (i.e., swing interval and stance interval). The gait rhythm standard deviation (¿) parameters computed with the PDFs indicated that the gait variability is significantly increased in PD. Signal turns count (STC) was also derived from each outlier-processed gait rhythm time series to serve as a dominant feature, which could be used to characterize the gait variability in PD. Since it was observed that the statistical parameters of swing interval or stance interval were highly correlated with those of stride interval, this article only used the stride interval parameters, i.e., ¿r and STCr , to form the feature vector in the pattern classification experiments. The results evaluated with the leave-one-out cross-validation method demonstrated that the least squares support vector machine with polynomial kernels was able to provide a classification accurate rate of 90.32% and an area (Az) of 0.952 under the receiver operating characteristic curve, both of which were better than the results obtained with the linear discriminant analysis (accuracy: 67.74%, Az: 0.917). The features and the classifiers used in the present study could be useful for monitoring of the gait in PD.

Biology is meaningful and important to identify cytokines and investigate their various functions and biochemical mechanisms. However, several issues remain, including the large scale of benchmark datasets, serious imbalance of data, and discovery of new gene families. In this paper, we employ the machine learning approach based on a novel ensemble classifier to predict cytokines. We directly selected amino acids sequences as research objects. First, we pretreated the benchmark data accurately. Next, we analyzed the physicochemical properties and distribution of whole amino acids and then extracted a group of 120-dimensional (120D) valid features to represent sequences. Third, in the view of the serious imbalance in benchmark datasets, we utilized a sampling approach based on the synthetic minority oversampling technique algorithm and K-means clustering undersampling algorithm to rebuild the training set. Finally, we built a library for dynamic selection and circulating combination based on clustering (LibD3C) and employed the new training set to realize cytokine classification. Experiments showed that the geometric mean of sensitivity and specificity obtained through our approach is as high as 93.3%, which proves that our approach is effective for identifying cytokines.