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The quality of object oriented information systems (OOIS) depends greatly on the decisions taken at early phases of their development. As an early available artifact the quality of the class diagram is crucial to the success of system development. Class diagrams lay the foundation for all later design work. So, their quality heavily affects the product that will be ultimately implemented. Even though the appearance of the Unified Modeling Language (UML) as a standard of modelling OOIS has contributed greatly towards building quality OOIS, it is not enough. Early availability of metrics is a key factor in the successful management of OOIS development. The aim of this paper is to present a set of metrics for measuring the structural complexity of UML class diagrams and to use them for predicting their maintainability that will heavily be correlated with OOIS maintainability.

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The incidence of tick-borne diseases (TBDs) is growing worldwide, and vaccines appear as the most effective and environmentally sound intervention for the prevention and control of TBDs. Areas covered: The vaccinomics approach combines omics technologies and bioinformatics for the characterization of tick-host-pathogen molecular interactions and the development of next-generation vaccines. The two main challenges of the vaccinomics approach are the integration and analysis of omics datasets, and the development of screening platforms for the identification of candidate protective antigens. To address these challenges we propose the application of intelligent Big Data analytic techniques for the high throughput discovery and characterization of tick and pathogen derived candidate vaccine protective antigens. Expert commentary: This innovative approach should improve the development and efficacy of vaccines for the control and prevention of TBDs.