Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be
used for predictive and descriptive modelling as well as for discriminative variable selection …

Among all the software packages available for discriminant analyses based on projection to
latent structures (PLS-DA) or orthogonal projection to latent structures (OPLS-DA), SIMCA …

Background and objective Computer automated diagnosis of various skin lesions through
medical dermoscopy images remains a challenging task. Methods In this work, we propose …

Background and objective Automatic segmentation of skin lesions in dermoscopy images is
still a challenging task due to the large shape variations and indistinct boundaries of the …

Motivation: Modern data acquisition based on high-throughput technology is often facing the
problem of missing data. Algorithms commonly used in the analysis of such large-scale data …

Metabolomics is increasingly being applied towards the identification of biomarkers for
disease diagnosis, prognosis and risk prediction. Unfortunately among the many published …

Data analysis is a very challenging task in LC-MS metabolomic studies. The use of powerful
analytical techniques (eg, high-resolution mass spectrometry) provides high-dimensional …

Abstract Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method
with a special binary 'dummy'y-variable and it is commonly used for classification purposes …

Repeated double cross validation (rdCV) is a strategy for (a) optimizing the complexity of
regression models and (b) for a realistic estimation of prediction errors when the model is …

In the last twenty years, the life sciences have seen a dramatic increase in the size and
number of data sets. Simple sensing devices in many cases offer real-time data streaming …