The large amounts of available protein structures emerges the need for computational methods for protein function prediction. Predicting protein function is mainly based on finding similarities between proteins with unknown function with already annotated proteins. This may be achieved using different protein characteristics: sequences, interactions, localization, structure and or psychochemical. A lot of review papers mainly focus on sequence and psychochemical features-based methods. This is because sequence and psychochemical data are easy to deal with and to interpret the results, and much available compared to protein structures. However, structure-based computational methods provide additional accuracy and reliability of protein function prediction. Therefore, unlike many review papers, this paper presents an up-to-date review on the structure-based protein function prediction. The aim was to provide a recent and comprehensive review of protein structure related topics: function aspects, structural classification, databases, tools and methods.

Association rules discovery is one of the vital data mining techniques. Currently there is an increasing interest in data mining and educational systems, making educational data mining (EDM) as a new growing research community. In this paper, we present a model for association rules discovery from King Abdulaziz University (KAU) admission system data. The main objective is to extract the rules and relations between admission system attributes for better analysis. The model utilizes an apriori algorithm for association rule mining. Detailed analysis and interpretation of the experimental results is presented with respect to admission office perspective.