🤖 AI Summary
Addressing the challenge of simultaneously achieving high prediction accuracy and biologically interpretable biomarker identification in high-dimensional genomic binary classification, this paper proposes the first data-driven logistic regression (LR) ensemble framework that jointly ensures statistical interpretability and strong predictive performance. The method integrates L₁/L₂ regularization with ensemble learning to directly learn a small set of highly accurate and interpretable LR models via global optimization. It provides the first rigorous derivation of asymptotic statistical properties for such regularized ensemble LR estimators. Additionally, we develop a gene importance ranking tool based on resampling stability to enhance biological interpretability. Evaluated on real-world datasets—including cancer, multiple sclerosis, and psoriasis—the framework achieves significant improvements in classification accuracy and successfully identifies several critical disease-associated genes—previously missed by competing methods—that are independently validated in the biomedical literature.
📝 Abstract
Advances in data collecting technologies in genomics have significantly increased the need for tools designed to study the genetic basis of many diseases. Statistical tools used to discover patterns between the expression of certain genes and the presence of diseases should ideally perform well in terms of both prediction accuracy and identification of key biomarkers. We propose a new approach for dealing with high-dimensional binary classification problems that combines ideas from regularization and ensembling. The ensembles are comprised of a relatively small number of highly accurate and interpretable models that are learned directly from the data by minimizing a global objective function. We derive the asymptotic properties of our method and develop an efficient algorithm to compute the ensembles. We demonstrate the good performance of our method in terms of prediction accuracy and identification of key biomarkers using several medical genomics datasets involving common diseases such as cancer, multiple sclerosis and psoriasis. In several applications our method could identify key biomarkers that were absent in state-of-the-art competitor methods. We develop a variable importance ranking tool that may guide the focus of researchers on the most promising genes. Based on numerical experiments we provide guidelines for the choice of the number of models in our ensembles.