Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC.

It is a challenging task for fundamental research whether proteins can interact with their partners. Protein self-interaction (SIP) is a special case of PPIs, which plays a key role in the regulation of cellular functions. Due to the limitations of experimental self-interaction identification, it is very important to develop an effective biological tool for predicting SIPs based on protein sequences. In the study, we developed a novel computational method called RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) for detecting SIPs from protein sequences. Firstly, Average Blocks (AB) feature extraction method is employed to represent protein sequences on a Position Specific Scoring Matrix (PSSM). Secondly, Principal Component Analysis (PCA) method is used to reduce the dimension of AB vector for reducing the influence of noise. Then, by employing the Relevance Vector Machine (RVM) algorithm, the performance of RVM-AB is assessed and compared with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on yeast and human datasets respectively. Using the fivefold test experiment, RVM-AB model achieved very high accuracies of 93.01% and 97.72% on yeast and human datasets respectively, which are significantly better than the method based on SVM classifier and other previous methods. The experimental results proved that the RVM-AB prediction model is efficient and robust. It can be an automatic decision support tool for detecting SIPs. For facilitating extensive studies for future proteomics research, the RVMAB server is freely available for academic use at http://219.219.62.123:8888/SIP_AB.

Improved dynamic ID-based authentication scheme for telecare medical information systems.

In order to protect users' identity privacy, Chen et al. proposed an efficient dynamic ID-based authentication scheme for telecare medical information systems. However, Chen et al.'s scheme has some weaknesses. In Chen et al.'s scheme, an attacker can track a user by a linkability attack or an off-line identity guessing attack. Chen et al.'s scheme is also vulnerable to an off-line password guessing attack and an undetectable on-line password guessing attack when user's smart card is stolen. In server side, Chen et al.'s scheme needs large computational load to authentication a legal user or reject an illegal user. To remedy the weaknesses in Chen et al.'s scheme, we propose an improved smart card based password authentication scheme. Our analysis shows that the improved scheme can overcome the weaknesses in Chen et al.'s scheme.