Personalised modelling offers a new and effective approach for the study in pattern recognition and knowledge discovery, especially for biomedical applications. The created models are more useful and informative for analysing and evaluating an individual data object for a given problem. Such models are also expected to achieve a higher degree of accuracy of prediction of outcome or classification than conventional systems and methodologies. Motivated by the concept of personalised medicine and utilising transductive reasoning, personalised modelling was recently proposed as a new method for knowledge discovery in biomedical applications. Personalised modelling aims to create a unique computational diagnostic or prognostic model for an individual. Here we introduce an integrated method for personalised modelling that applies global optimisation of variables (features) and an appropriate size of neighbourhood to create an accurate personalised model for an individual. This method creates an integrated computational system that combines different information processing techniques, applied at different stages of data analysis, e.g. feature selection, classification, discovering the interaction of genes, outcome prediction, personalised profiling and visualisation, etc. It allows for adaptation, monitoring and improvement of an individual’s model and leads to improved accuracy and unique personalised profiling that could be used for personalised treatment and personalised drug design.