Conotoxins are small disulfide-rich peptides that are invaluable channel-targeted peptides and target neuronal receptors. They show prospects for being potent pharmaceuticals in the treatment of Alzheimer's disease, Parkinson's disease, and epilepsy. Accurate and fast prediction of conotoxin superfamily is very helpful towards the understanding of its biological and pharmacological functions especially in the post-genomic era. In the present study, we have developed a novel approach called PredCSF for predicting the conotoxin superfamily from the amino acid sequence directly based on fusing different kinds of sequential features by using modified one-versus-rest SVMs. The input features to the PredCSF classifiers are composed of physicochemical properties, evolutionary information, predicted second structure and amino acid composition, where the most important features are further screened by random forest feature selection to improve the prediction performance. The prediction results show that PredCSF can obtain an overall accuracy of 90.65% based on a benchmark dataset constructed from the most recent database, which consists of 4 main conotoxin superfamilies and 1 class of non-conotoxin class. Systematic experiments also show that combing different features is helpful for enhancing the prediction power when dealing with complex biological problems. PredCSF is expected to be a powerful tool for in silico identification of novel conotonxins and is freely available for academic use at http://www.csbio.sjtu.edu.cn/bioinf/PredCSF.

In this paper, we propose a Geometric Moving Average Martingale (GMAM) method for change detection. There are two components underpinning the method which enable it to reduce false detections. The first is the exponential weighting of observations to obtain the GMAM value and the second is the use of the value for hypothesis testing to determine whether a change has occurred. Extension of the GMAM method to the average GMAM (AG) method has been applied to analyze seismic anomalies within outgoing long-wave radiation (OLR) data observed by satellites from 2006 to 2013 for the two recent Wenchuan and Lushan earthquakes and four comparative study areas: Wenchuan, Puer, Beijing, and Northeastern areas. The Yushu earthquake and Hetian earthquake have also been examined. The experimental results show that the proposed AG method can effectively extract abnormal changes within OLR data and that there are large AG values in the pre and postoccurrence of the earthquakes in these areas, which could be viewed as seismic anomalies, and the AG method has experimentally compared with the deviation method. The experimental results show that the AG method can effectively reflect the change process in OLR data.

This paper presents a new weighted local outlier factor method for anomaly detection, which is underpinned with three novel components: (1) a piecewise linear representation defined on the basis of the important points that consist of extreme points and additional points; (2) a set of new features which are used to identify anomalies given the new piecewise linear representation; (3) a weighting schema, assigning different weights to different features by accounting for the discriminant power of the features. The underlying idea of the proposed method is to characterize a time series with a set of four features and then discover abnormal changes by taking account of the closeness of any data points augmented with the new features. The comparative experiments demonstrate that the proposed piecewise representation method has performed well in sequential time series data, and the weighted local outlier factor method has achieved better accuracy and RankPower in detecting anomalies from the same data sets in comparison with the conventional local outlier factor, normalized local outlier factor and HOT symbolic aggregate approximation methods.

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