Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
This provides data-driven guidance for bioinformatics researchers applying machine learning, but it is incremental as it focuses on benchmarking and tuning existing methods.
The authors tackled the problem of algorithm selection in bioinformatics by analyzing 13 state-of-the-art machine learning algorithms on 165 classification problems, resulting in recommendations for five algorithms with optimized hyperparameters to maximize performance.
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 machine learning algorithms on a set of 165 publicly available classification problems in order to provide data-driven algorithm recommendations to current researchers. We present a number of statistical and visual comparisons of algorithm performance and quantify the effect of model selection and algorithm tuning for each algorithm and dataset. The analysis culminates in the recommendation of five algorithms with hyperparameters that maximize classifier performance across the tested problems, as well as general guidelines for applying machine learning to supervised classification problems.