Cited by 287Machine Learning in BioinformaticsMachine Learning and Data ClassificationGene expression and cancer classification

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Data-driven advice for applying machine learning to bioinformatics problems

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Olson, Randal S., Cava, William La, Mustahsan, Zairah, Varik, Akshay, Moore, Jason H.·2018·Biocomputing 2018

DOI 10.1142/9789813235533_0018PMC PMC5890912

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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.

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M, A. M. A., Mythreyi, S. N., Suresh, R., Udayakumar, U., G.Saravanan, & Ghildiyal, N. (2026) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit69683.2026.11620621
Medical-data classification system using AI-based ensemble learning methods
Chen, S.-T., Lu, H., Tsai, C.-Y., Wu, J., Lin, Y.-W., & Liang, L. (2026) · Open Computer Science · doi:10.1515/comp-2025-0062

Authors: Olson, R. S., Cava, W. L., Mustahsan, Z., Varik, A., & Moore, J. H.
Affiliations: University of Pennsylvania (US)
ORCID: Olson, Randal S.
Cava, William La
Moore, Jason H.
Venue: Biocomputing 2018
Publisher: WORLD SCIENTIFIC
Published: 2017-11-17
Volume: 23
Pages: 192-203
DOI: 10.1142/9789813235533_0018
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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