Predictive modeling of therapy response in multiple sclerosis using gene expression data

Transcription profiling studies reveal important insights in regards to molecular events that manifest in phenotypic outcomes such as response to drug therapy. Construction of computational models that accurately predict therapy response is only possible when precise data measurements, robust feature/gene selection, and advanced computational modeling methods are combined with stringent statistical validation and large scale verification of results. Due to the large number of gene expression measurements in transcriptional profiling studies, feature selection represents a bottleneck when constructing computational models. The degree of compromise between selection of the optimal feature set and computational efficiency results in many choices for candidate gene sets which leads to a wide range of classification accuracies. Furthermore, constructing a classification model using a larger-than-necessary gene set along with small number of samples may cause over-fitting the data, resulting in highly optimistic classification accuracies. In this study we present OSeMA, a fast, robust and accurate gene selection-classification framework which results in construction of classification models that are highly predictive of the rIFNB therapy response in multiple sclerosis patients. We assess the performance of OSeMA on held out test data. Additionally, we extensively evaluate OSeMA by comparing it to an exhaustive combinatorial gene selection-classification approach