The TSP method compared favourably to the estimated accuracy of standard clinical methods for the differentia tion of viral and bacterial infection, as well as cardiomy opathy classification conditions that present ongoing diagnostic challenges in the clinic. For example, a recently developed clinical prediction rule to discriminate between bacterial and viral pneumonia in children achieved positive predictive value of under 80%, in con trast to a TSP classifier cross validation accuracy of 96. 7%. Additionally, a recent study of over 1200 patients presenting with diverse cardiomyopathies found that no pathologic etiology could be definitively elucidated in over 50% of clinical cases, in comparison with a cross val idation accuracy of over 70% achieved by the correspond ing TSP classifier.

These results do not imply that the TSP method provides intrinsically superior diagnostic dis crimination to gold standard clinical measures the TSP classifiers themselves are constrained by the fidelity of clinical methods used to diagnose patient samples con tained within their respective training datasets. However, these results do indicate that properly trained TSP classifi ers may exhibit higher accuracy in medical contexts where high fidelity diagnoses are difficult or impractical to regu larly obtain using other methods. Interestingly, the ability of the classifier to obtain an accu rate diagnosis was significantly lower in the comparison of ischemic and idiopathic cardiomyopathies than in any other dataset we examined.

Carfilzomib This is likely due to the broad cellular and metabolic heterogeneity observed in these two closely related conditions. Both clinical and molecu lar differentiation of ischemic and idiopathic cardiomy opathies remains a significant challenge. Ischemic cardiomyopathy is diagnosed when oxygen delivery to the myocardium is inhibited, most often due to coronary artery disease. However, the presence of this condition is not diagnosed with great precision in the clinic, and idio pathic cardiomyopathy is diagnosed when no etiological factor for cardiovascular dysfunction can be explicitly iso lated. The failure of the algorithm to accurately dis criminate between these two conditions may indicate that they represent overlapping genetic and physiological states, or that their respective diagnoses are not made with high fidelity in clinic, or a combination of both factors.

This molecular heterogeneity has recently been confirmed using alternative gene expression analysis methods. It is possible that other factors, such as consistency of tissue collection and processing, may negatively impact the quality of microarray data and thus the apparent perform Top Scoring Classifiers and Distributions of Classifier Accuracies ance of the algorithm.