Personalized Information Modeling for Personalized Medicine

  • Yingjie Hu
  • Nikola Kasabov
  • Wen Liang
Part of the Springer Handbooks book series (SHB)


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 individualʼs model and leads to improved accuracy and unique personalized profiling that could be used for personalized treatment and personalized drug design.





absolute error


area under curve


breast cancer-associated gene


deoxyribonucleic acid


evolving classification function


evolutionary strategy


genetic algorithm


genome-wide association scan


integrated method for personalized modeling


integrated optimization system for personalized modeling


K nearest neighbor


leave-one-out cross validation


multilayer perceptron


multiple linear regression


personalized modeling framework


personalized modeling system


radial basis function


root mean squared error


ribonucleic acid


single-nucleotide polymorphism


signal-to-noise ratio


self-organizing map


support vector machine


transductive weighted neuro-fuzzy inference engine


transductive inference based radial basis function


weighted nearest neighbor


weighted distance and weighted variables K nearest neighbor


coevolutionary based algorithm for personalized modeling


messenger RNA


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Copyright information

© Springer-Verlag 2014

Authors and Affiliations

  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  2. 2.KEDRI – Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand
  3. 3.School of Computing and Mathematical ScienceAuckland University of TechnologyAucklandNew Zealand

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