Soft Computing Methods for Global, Local and Personalized Modeling and Applications in Bioinformatics

The paper is a comparative study of major modeling and pattern discovery approaches applicable to the area of Bioinformatics and the area of decision support systems in general. These approaches include inductive versus transductive reasoning, global, local, and personalized modeling and their potentials 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. The paper uses several techniques to illustrate these approaches – multiple linear regression, Bayesian inference, support vector machines, evolving connectionist systems (ECOS), weighted kNN – each of them providing different accuracy on specific problem and facilitating the discovery of different patterns and rules from data.

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