The cancerlectin plays a key role in the process of tumor cell differentiation. Thus, to fully understand the function of cancerlectin is significant because it sheds light on the future direction for the cancer therapy. However, the traditional wet-experimental methods were money- and time-consuming. It is highly desirable to develop an effective and efficient computational tool to identify cancerlectins. In this study, we developed a sequence-based method to discriminate between cancerlectins and non-cancerlectins. The analysis of variance (ANOVA) was used to choose the optimal feature set derived from the g-gap dipeptide composition. The jackknife cross-validated results showed that the proposed method achieved the accuracy of 75.19%, which is superior to other published methods. For the convenience of other researchers, an online web-server CaLecPred was established and can be freely accessed from the website http://lin.uestc.edu.cn/server/CalecPred. We believe that the CaLecPred is a powerful tool to study cancerlectins and to guide the related experimental validations.

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Insider threat has always been an important hidden danger of information system security, and the detection of insider threat is the main concern of information system organizers. Before the anomaly detection, the process of feature extraction often causes a part of information loss, and the detection of insider threats in a single time point often causes false positives. Therefore, this paper proposes a user behavior analysis model, by aggregating user behavior in a period of time, comprehensively characterizing user attributes, and then detecting internal attacks. Firstly, the user behavior characteristics are extracted from the multi-domain features extracted from the audit log, and then the XGBoost algorithm is used to train. The experimental results on a user behavior dataset show that the XGBoost algorithm can be used to identify the insider threats. The value of F-measure is up to 99.96% which is better than SVM and random forest algorithm.

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