Methods of identifying biological patterns using multiple data sets

Abstract

Systems and methods for enhancing knowledge discovery from data using multiple learning machines in general and multiple support vector machines in particular. Training data for a learning machine is pre-processed in order to add meaning thereto. Multiple support vector machines, each comprising distinct kernels, are trained with the pre-processed training data and are tested with test data that is pre-processed in the same manner. The test outputs from multiple support vector machines are compared in order to determine which of the test outputs if any represents a optimal solution. Selection of one or more kernels may be adjusted and one or more support vector machines may be retrained and retested. Optimal solutions based on distinct input data sets may be combined to form a new input data set to be input into one or more additional support vector machine. The methods, systems and devices of the present invention comprise use of Support Vector Machines for the identification of patterns that are important for medical diagnosis, prognosis and treatment. Such patterns may be found in many different datasets. The present invention also comprises methods and compositions for the treatment and diagnosis of medical conditions.