Driven by high-throughput sequencing techniques, modern genomic and clinical studies are
in a strong need of integrative machine learning models for better use of vast volumes of …

Correlation and association analyses are one of the most widely used statistical methods in
research fields, including microbiome and integrative multiomics studies. Correlation and …

The gut microbiota shapes the response to immune checkpoint inhibitors (ICIs) in cancer,
however dietary and geographic influences have not been well-studied in prospective trials …

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 …

Abstract Background Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular
machine learning tool that is gaining increasing attention as a useful feature selector and …

Increasing incidence of inflammatory bowel diseases, such as Crohn's disease, in
developed nations is associated with changes to the microbial environment, such as …

In contrast to conventional optical imaging modalities, hyperspectral imaging (HSI) is able to
capture much more information from a certain scene, both within and beyond the visual …

Background Variable selection on high throughput biological data, such as gene expression
or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant …

The advent of large-scale microbiome studies affords newfound analytical opportunities to
understand how these communities of microbes operate and relate to their environment …

Since the publication of the second edition, many new Bayesian tools and methods have
been developed for space-time data analysis, the predictive modeling of health outcomes …