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 …

This review focuses on recent and potential advances in chemometric methods in relation to
data processing in metabolomics, especially for data generated from mass spectrometric …

Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of
effective methods which can select an optimal variables subset. In this study, a strategy that …

The identification of disease-relevant genes represents a challenge in microarray-based
disease diagnosis where the sample size is often limited. Among established methods …

Wavelength selection is a critical step for producing better prediction performance when
applied to spectral data. Considering the fact that the vibrational and rotational spectra have …

Partial least squares (PLS) have gained wide applications especially in chemometrics,
metabolomics/metabonomics as well as bioinformatics. Here, we present libPLS, a library …

Model-population analysis (MPA) was recently proposed as a general framework for
designing new types of chemometrics and bioinformatics algorithms, and it has found …

Variable (wavelength or feature) selection techniques have become a critical step for the
analysis of datasets with high number of variables and relatively few samples. In this study, a …

Selecting a small number of informative genes for microarray-based tumor classification is
central to cancer prediction and treatment. Based on model population analysis, here we …

When analyzing high-dimensional near-infrared (NIR) spectral datasets, variable selection
is critical to improving models' predictive abilities. However, some methods have many …