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Data-driven advice for applying machine learning to bioinformatics problems
Open accessAbstract
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.
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“Although prior work has found that some fields will benefit from the use of ML methods,84”
From Leakage and the reproducibility crisis in machine-learning-based science (2023) · cites this paper as [84]
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“Extreme gradient boosting algorithm (DART [Dropouts meet Multiple Additive Regression Trees]) was used for model training, as it was the most efficient method in similar studies [33,37,58].”
From Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling (2021) · cites this paper as [58]
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Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
Olson, R. S., Bartley, N., Urbanowicz, R. J., & Moore, J. H. (2016) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2908812.2908918 · Cited by 679
Self-citationDetecting Cancer Metastases on Gigapixel Pathology Images
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Velez, D. R., White, B. C., Motsinger‐Reif, A. A., Bush, W. S., Ritchie, M. D., Williams, S. M., & Moore, J. H. (2007) · Genetic Epidemiology · doi:10.1002/gepi.20211 · Cited by 386
Self-citationPMLB: a large benchmark suite for machine learning evaluation and comparison
Olson, R. S., Cava, W. L., Orzechowski, P., Urbanowicz, R. J., & Moore, J. H. (2017) · BioData Mining · doi:10.1186/s13040-017-0154-4 · Cited by 373
Self-citationMachine Learning for Detecting Gene-Gene Interactions
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An empirical comparison of supervised machine learning techniques in bioinformatics
Tan, A. C., & Gilbert, D. (2003) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 62
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Cited by
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A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127
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Metadata
- Authors
- Olson, R. S., Cava, W. L., Mustahsan, Z., Varik, A., & Moore, J. H.
- Affiliations
- University of Pennsylvania (US)
- Venue
- Biocomputing 2018
- Publisher
- WORLD SCIENTIFIC
- Published
- 2017-11-17
- Volume
- 23
- Pages
- 192-203
- PMC
- PMC5890912
- OpenAlex
- W2749093116
- Cited by
- 287
- References
- 4
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 33 · 2023: 32 · 2022: 50 · 2021: 43 · 2020: 48 · 2019: 31 · 2018: 24
- FWCI
- 104.80
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Pacific Symposium on Biocomputing (PSB) (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- University of Pennsylvania (ES013508)
- University of the Pacific
- University of Pennsylvania Health System
- National Institutes of Health (1uc4dk112217-01, DK112217, ES013508)
- National Institute of Allergy and Infectious Diseases (R01AI116794)
- National Institute of Diabetes and Digestive and Kidney Diseases (UC4-DK-112217, U01DK112217)
- National Institute of Environmental Health Sciences (P30ES013508)
- U.S. National Library of Medicine (R01LM012601)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- machine learning algorithm comparison, bioinformatics classification, algorithmic recommendation, hyperparameter optimization, model selection, supervised classification, classification performance, data-driven recommendation
- MeSH terms
- Supervised Machine Learning, Algorithms, Humans, Linear Models, Databases, Factual, Nonlinear Dynamics, Computational Biology
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