In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …

Cancer is a dangerous disease that causes death worldwide. Discovering few genes
relevant to one cancer disease can result in effective treatments. The challenge associated …

Feature selection techniques have become an apparent need in many bioinformatics
applications. In addition to the large pool of techniques that have already been developed in …

Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets
with increased complexity, making feature selection an essential technique for single-cell …

DNA microarray technology has now made it possible to simultaneously monitor the
expression levels of thousands of genes during important biological processes and across …

This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

We present an algorithmic framework for learning local causal structure around target
variables of interest in the form of direct causes/effects and Markov blankets applicable to …

Ensemble learning is an intensively studied technique in machine learning and pattern
recognition. Recent work in computational biology has seen an increasing use of ensemble …

Abstract Background The Cancer Genome Atlas (TCGA) has generated comprehensive
molecular profiles. We aim to identify a set of genes whose expression patterns can …