With the increasing ease of measuring multiple variables per object the importance of
variable selection for data reduction and for improved interpretability is gaining importance …

High dimensional statistical problems arise from diverse fields of scientific research and
technological development. Variable selection plays a pivotal role in contemporary statistical …

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep
biological understanding requires more than a taxonomic listing of clusters. As new methods …

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied
to experiments representing a single condition, technology, or species to discover and …

This is a book on data analysis with a specific focus on the practice of predictive modeling.
The term predictive modeling may stir associations such as machine learning, pattern …

The caret package, short for classification and regression training, contains numerous tools
for develo** predictive models using the rich set of models available in R. The package …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD)
are not well understood. Using a large neuroimaging dataset, we identified three latent …

We present a penalized matrix decomposition (PMD), a new framework for computing a rank-
K approximation for a matrix. We approximate the matrix X as, where dk, uk, and vk minimize …

The support vector machine (SVM) has become one of the standard tools for machine
learning and data mining. This carefully edited volume presents the state of the art of the …