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The paper is offering a comparative study of major modeling and pattern discovery approaches applicable to the area of data analysis and decision support systems in general, and to the area of Bioinformatics and Medicine - in particular. Compared are inductive versus transductive reasoning, global, local, and personalised modeling, and all these approaches are illustrated on a case study of gene expression and clinical data related to cancer outcome prognosis. While inductive modeling is used to develop a model (function) from data on the whole problem space and then to recall it on new data, transductive modeling is concerned with the creation of single model for every new input vector based on some closest vectors from the existing problem space. A new method - WWKNN (weighted distance, weighted variables K-nearest neighbors), and a framework for the integration of global, local and personalised models for a single input vector are proposed. Integration of data (e.g. clinical and genetic) and of models (e.g. global, local and personalised) for a better pattern discovery, adaptation and accuracy of the results, are the major points of the paper.