Making contract users safer: Towards building a Safe Browsing platform on Ethereum.

There are many papers and tools regarding the detection of unsafe contracts, but few ways for detection results to practically benefit contract users and owners. This paper presents a Blockchain Safe Browsing (BSB) platform to safely disseminate those detection results. An encrypted blacklist will be generated to provide privacy preserving user warning before they make transactions with unsafe contracts. Contract owners will be notified that there are vulnerabilities in their contracts, and they can purchase related reports which record how to exploit the vulnerabilities. The profits inspire the researchers to contribute their update-to-date lists of unsafe contracts. An effective encryption scheme is developed to guarantee that only contract owners can decrypt the encrypted reports. Extensive evaluations demonstrate that our prototype can function as intended without sacrificing user experience.

Computational prediction of conformational B-cell epitopes from antigen primary structures by ensemble learning.

MOTIVATION: The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences. METHODS: In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences. RESULTS: Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction. AVAILABILITY: The web server and datasets are freely available at http://bcell.whu.edu.cn.