Abstract: A clinical decision support system for diagnosis and monitoring of a disease of at least one patient includes a computing system, a storage media, in communication with the computer system, configured to store medical data sets from two or more different medical training data sources, where each of the two or more different medical training data sources have uniquely defined objective findings influencing the disease. The severity of the objective findings within each data source is indicated by independent weight factors. A computer program operating on the computing system is configured to perform a knowledge mapping between the data in the two or more different medical training data sources for obtaining plurality of clinical weight factors that define a unique classification rules. An input module is provided for receiving medical data about a patient, and a processor processes the received medical data from the input module.

Abstract: The present teachings describe methods for selecting informative genetic markers including single nucleotide polymorphisms (SNPs) that may be used in the design and execution of genome wide association studies. These methods are distinguished from other methods relying on a predefined haplotype block structure and may be configured to make use of correlations that occur across neighboring haplotype blocks. The disclosed methods may further be implemented across chromosomal regions having both high and low local linkage disequilibrium. Informative genetic marker selection, as described, provides an alternative and potentially more efficient mechanism to select genetic markers such as SNPs using block-based and random approaches.

Abstract: A method for identifying a protein including cleaving the protein with a proteolytic agent to produce peptide fragments, providing an array comprising a solution set of binding reagents, contacting the peptide fragments with the array to promote specific interactions between the fragments and the array, detecting the binding pattern of the peptide fragments on the array, and comparing the binding pattern of the peptide fragments to a reference set. A solution set refers to a set of binding reagents, or epitopes associated with such binding reagents, that can identify members of a given protein mixture or protein catalog using a minimal number of binding reagents (or epitopes corresponding to the binding reagents) based on certain constraints. The solution set can be determined using a randomized greedy algorithm. The solution set can be refined using a local search algorithm.