Interpretability and explainability are crucial for machine learning (ML) and statistical
applications in medicine, economics, law, and natural sciences and form an essential …

In this review, we examine the problem of designing interpretable and explainable machine
learning models. Interpretability and explainability lie at the core of many machine learning …

Study of human disease remains challenging due to convoluted disease etiologies and
complex molecular mechanisms at genetic, genomic, and proteomic levels. Many machine …

Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been
created in recent years, with the vast majority based on regularized linear regression. This …

We introduce the concrete autoencoder, an end-to-end differentiable method for global
feature selection, which efficiently identifies a subset of the most informative features and …

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools,
not only to estimate chronological and biological age but also to assess the efficacy of anti …

This article introduces a machine for sampling approximate model-X knockoffs for arbitrary
and unspecified data distributions using deep generative models. The main idea is to …

Background In health research, multimodal omics data analysis is widely used to address
important clinical and biological questions. Traditional statistical methods rely on the strong …

Abstract Machine learning models exhibit strong performance on datasets with abundant
labeled samples. However, for tabular datasets with extremely high $ d $-dimensional …

Academic tabular benchmarks often contain small sets of curated features. In contrast, data
scientists typically collect as many features as possible into their datasets, and even …