This paper provides a brief survey of the basic concepts and algorithms used for Machine
Learning and its applications. We begin with a broader definition of machine learning and …

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved
its effectiveness in bioinformatics due to its capacity to produce local instead of global …

Motivation: Binary datasets represent a compact and simple way to store data about the
relationships between a group of objects and their possible properties. In the last few years …

Motivation: Over the past decade, several biclustering approaches have been published in
the field of gene expression data analysis. Despite of huge diversity regarding the …

Background The analysis of data generated by microarray technology is very useful to
understand how the genetic information becomes functional gene products. Biclustering …

This paper proposes a Bayesian nonparametric framework for clustering axially symmetric
data. Our approach is based on a Dirichlet processes mixture model with Watson …

Background One of the emerging techniques for performing the analysis of the DNA
microarray data known as biclustering is the search of subsets of genes and conditions …

Summary Mice lacking caveolin-1 (Cav1), a key protein of plasma membrane, exhibit brain
aging at an early adult stage. Here, integrative analyses of metabolomics, transcriptomics …

The microarray technology enables the analysis of the gene expression data and the
understanding of the important biological processes in an efficient way. We have developed …

How fetal brain development is regulated at the molecular level is not well understood. Due
to ethical challenges associated with research on the human fetus, large animals particularly …