Data-driven advice for applying machine learning to bioinformatics problems
Year: 2017
Type: conference-paper
Abstract: 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.
Institution: University of Pennsylvania
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Funders: University of Pennsylvania, University of the Pacific, University of Pennsylvania Health System, National Institutes of Health, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Environmental Health Sciences, U.S. National Library of Medicine
Awards: G1080356687, G1233929309, G142613954, G2180887151, Penn integrated Human Pancreas procurement and Analysis Program, Biomedical Computing and Informatics Strategies for Infectious Disease Research, G842207449, G9000155670, G9000233189, G9000341833, G9000352235, Center of Excellence in Environmental Toxicology, Human Pancreas Analysis Program for Type 1 Diabetes - HPAP-T1D
OpenAlex ID: W2749093116