Journal Papers Strengths and limitations of microarray-based phenotype prediction: Lessons learned from the IMPROVER Diagnostic Signature Challenge


Motivation: After more than a decade since microarrays were used to predict phenotype of biological samples, real life applications for disease screening and identification of patients that would best benefit of treatment are still emerging. The interest of the scientific community in identifying best approaches to develop such prediction models was reaffirmed in a competition style international collaboration called IMPROVER Diagnostic Signature Challenge whose results we describe herein. Results: Fifty-four teams used public data to develop prediction models in four disease areas including Multiple Sclerosis, Lung Cancer, Psoriasis, and Chronic Obstructive Pulmonary Disease, and made predictions on blinded new data that we generated. Teams were scored using three metrics that captured various aspects of the quality of predictions, and best performers were awarded. This article presents the challenge results and introduces to the community the approaches of the best overall three performers, as well as an R package that implements the approach of the best overall team. The analyses of model performance data submitted in the challenge as well as additional simulations that we have performed revealed that i) the quality of predictions depend more on the disease endpoint than on the particular approaches used in the challenge ii) the most important modeling factor (e.g. data preprocessing, feature selection and classifier type) are problem dependent and hence iii) for optimal results datasets and methods have to be carefully matched. Biomedical factors such as the disease severity and confidence in diagnostic were found to be associated with the misclassification rates across the different teams.

Paper Details


A. Tarca,  M. Lauria,  M. Unger,  E. Bilal,  S. Boue,  K. Dey,  J. Hoeng,  H. Koeppl,  F. Martin,  P. Meyer,  P. Nandy,  R. Norel,  M. Peitsch,  J. Rice,  R. Romero,  G. Stolovitzky,  M. Talikka,  Y. Xiang,  C. Zechne


Bioinformatics, 29, , 2892-9