Background Many existing bioinformatics predictors are based on machine learning
technology. When applying these predictors in practical studies, their predictive …

Machine learning is used in a large number of bioinformatics applications and studies. The
application of machine learning techniques in other areas such as pattern recognition has …

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and
proteomic data. Cross-validation methods are often used to obtain estimates of classification …

IntegratedMRF is an open-source R implementation for integrating drug response
predictions from various genomic characterizations using univariate or multivariate random …

Abstract Machine learning has become a pivotal tool for many projects in computational
biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical …

Background: The wide availability of genome-scale data for several organisms has
stimulated interest in computational approaches to gene function prediction. Diverse …

As the bioinformatics field grows, it must keep pace not only with new data but with new
algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used …

Background As in many different areas of science and technology, most important problems
in bioinformatics rely on the proper development and assessment of binary classifiers. A …

Research in bioinformatics is driven by the experimental data. Current biological databases
are populated by vast amounts of experimental data. Machine learning has been widely …

The increase in the number and complexity of biological databases has raised the need for
modern and powerful data analysis tools and techniques. In order to fulfill these …