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Cited by 2,127Machine Learning in BioinformaticsGene expression and cancer classificationNeural Networks and Applications

A guide to machine learning for biologists

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Greener, Joe G., Kandathil, Shaun M., Moffat, Lewis, Jones, David T.·2022·Nature Reviews Molecular Cell Biology
DOI 10.1038/s41580-021-00407-0
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  • Compared with traditional methods, machine learning excels in handling larger datasets and delivering accurate predictive outcomes more efficiently, with reduced bias.

    “Compared with traditional methods, ML excels in handling larger datasets and delivering accurate predictive outcomes more efficiently, with reduced bias [10].”

    From Machine learning with the body roundness index and associated indicators: a new approach to predicting metabolic syndrome (2025) · cites this paper as [10]

    The Introduction (page not supplied) says machine learning is useful for datasets too large or complex for human analysis and can make analysis reproducible and time-efficient. It does not establish that ML generally produces more accurate predictions or reduces bias compared with traditional methods. The Traditional machine learning section also cautions that deep learning can take more time and computation than traditional ML methods. (LLM, conf=0.86)

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Cited by

Sorted by citations

Authors
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T.
Affiliations
University College London (GB)
Corresponding author
Jones, David T.
ORCID
Venue
Nature Reviews Molecular Cell Biology
Publisher
Springer Science and Business Media LLC
Published
2021-09-13
Volume
23
Issue
1
Pages
40-55
ISSN
  • 1471-0072 (ISSN-L)
  • 1471-0080
DOI
10.1038/s41580-021-00407-0
OpenAlex
W3200707343
Cited by
2,127
References
25
Citations by year
2026: 382 · 2025: 611 · 2024: 551 · 2023: 380 · 2022: 195 · 2021: 6
FWCI
30.05
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Reviews Molecular Cell Biology (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords
machine learning, biological data analysis, deep neural network, predictive modeling
MeSH terms
Machine Learning, Deep Learning, Animals, Biology, Humans, Neural Networks, Computer

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