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Advances in data collecting technologies in genomics have significantlyincreased the need for tools designed to study the genetic basis of manydiseases. Statistical tools used to discover patterns between the expression ofcertain genes and the presence of diseases should ideally perform well in termsof both prediction accuracy and identification of key biomarkers. We propose anew approach for dealing with high-dimensional binary classification problemsthat combines ideas from regularization and ensembling. The ensembles arecomprised of a relatively small number of highly accurate and interpretablemodels that are learned directly from the data by minimizing a global objectivefunction. We derive the asymptotic properties of our method and develop anefficient algorithm to compute the ensembles. We demonstrate the goodperformance of our method in terms of prediction accuracy and identification ofkey biomarkers using several medical genomics datasets involving commondiseases such as cancer, multiple sclerosis and psoriasis. In severalapplications our method could identify key biomarkers that were absent instate-of-the-art competitor methods. We develop a variable importance rankingtool that may guide the focus of researchers on the most promising genes. Basedon numerical experiments we provide guidelines for the choice of the number ofmodels in our ensembles.

Data-Driven Logistic Regression Ensembles With Applications in Genomics