Background The selection, development, or comparison of machine learning methods in
data mining can be a difficult task based on the target problem and goals of a particular …

Motivation Novel machine learning and statistical modeling studies rely on standardized
comparisons to existing methods using well-studied benchmark datasets. Few tools exist …

In the past several years, the field of machine learning has seen an explosion of interest and
excitement, with hundreds or thousands of algorithms developed for different tasks every …

In computational biology and other sciences, researchers are frequently faced with a choice
between several computational methods for performing data analyses. Benchmarking …

Machine learning research depends on objectively interpretable, comparable, and
reproducible algorithm benchmarks. We advocate the use of curated, comprehensive suites …

We advocate the use of curated, comprehensive benchmark suites of machine learning
datasets, backed by standardized OpenML-based interfaces and complementary software …

Abstract Machine learning (ML) has become an essential asset for the life sciences and
medicine. We selected 250 articles describing ML applications from 17 journals sampling 26 …

This authoritative, expanded and updated second edition of Encyclopedia of Machine
Learning and Data Mining provides easy access to core information for those seeking entry …

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 …

Many scientific communities have expressed a growing interest in machine learning
algorithms recently, mainly due to the generally good results they provide, compared to …