Personalized information modeling for personalized medicine

Abstract

Personalized modeling offers a new and effective approach for the study of pattern recognition and knowledge discovery, especially for biomedical applications. The created models are very useful and informative for analyzing 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 personalized medicine and utilizing transductive reasoning, personalized modeling was recently proposed as a new method for knowledge discovery in biomedical applications. Personalized modeling aims to create a unique computational diagnostic or prognostic model for an individual. Here we introduce an integrated method for personalized modeling that applies global optimization of variables (features) and an appropriate neighborhood size to create an accurate personalized 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, personalized profiling and visualization, etc. It allows for adaptation, monitoring, and improvement of an individual’s model and leads to improved accuracy and unique personalized profiling that could be used for personalized treatment and personalized drug design.