Multi-omics data analysis is an important aspect of cancer molecular biology studies and
has led to ground-breaking discoveries. Many efforts have been made to develop machine …

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze
metabolomics datasets (indeed, it is the most well-known tool to perform classification and …

Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …

This paper presents two novel statistical analyses of multiblock data using the R language. It
is designed for data organized in (K+ 1) blocks (ie, tables) consisting of a block of response …

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 ever increasing interest of consumers for safety, authenticity and quality of food
commodities has driven the attention towards the analytical techniques used for analyzing …

In recent years, multi-modal measurements of process and product properties have become
widely popular. Sometimes classical chemometric methods such as principal component …

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells
and biological fluids, free of observational biases inherent to more focused studies of …

This paper provides a state-of-the-art review of the methods and applications of data-driven
fault detection and diagnosis that have been developed over the last two decades. The …

Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …