Partial least squares (PLS) is an efficient statistical regression technique that is highly suited
for the analysis of genomic and proteomic data. In this article, we review both the theory …

Applications of clustering algorithms in biomedical research are ubiquitous, with typical
examples including gene expression data analysis, genomic sequence analysis, biomedical …

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

This paper presents an overview of the image analysis techniques in the domain of
histopathology, specifically, for the objective of automated carcinoma detection and …

At present, variable selection turns to prominence since it obviously alleviate a trouble of
measuring multiple variables per sample. The partial least squares regression (PLS-R) and …

In bioinformatics studies, supervised classification with high-dimensional input variables is
frequently encountered. Examples routinely arise in genomic, epigenetic and proteomic …

Esophageal cancer is the sixth leading cause of death from cancer and one of the least
studied cancers worldwide. The global microRNA expression profile of esophageal cancer …

Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental
disorders in which known mutations contribute to disease risk in 20% of cases. Here, we …

Mass-spectra based proteomic profiles have received widespread attention as potential
tools for biomarker discovery and early disease diagnosis. A major data-analytical problem …

In this paper, a novel Multi Class based Feature Extraction (MC-FE) method has been
proposed for medical data classification. Genomic datasets, or gene expression-based …