Related Experiment Video
Updated: May 8, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Strengths and limitations of microarray-based phenotype prediction: lessons learned from the IMPROVER Diagnostic
Adi L Tarca1, Mario Lauria, Michael Unger
1Department of Computer Science, Wayne State University, Perinatology Research Branch, NICHD/NIH, Detroit, MI 48201, USA, The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Rovereto 38068, Italy, ETH Zurich, Zurich 8092, Switzerland, IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598, USA and Philip Morris International, Research & Development, Neuchâtel CH-2000, Switzerland.
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 who would best benefit from 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 depends 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) is problem dependent; and (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.
Availability:
The lung cancer dataset is available from Gene Expression Omnibus (accession, GSE43580). The maPredictDSC R package implementing the approach of the best overall team is available at www.bioconductor.org or http://bioinformaticsprb.med.wayne.edu/.
