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Cited by 1,473Machine Learning in BioinformaticsBioinformatics and Genomic NetworksGenomics and Chromatin Dynamics

Deep learning for computational biology

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Angermueller, Christof, Pärnamaa, Tanel, Parts, Leopold, Stegle, Oliver·2016·Molecular Systems Biology
DOI 10.15252/msb.20156651PMC PMC4965871
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Abstract

Technological advances in genomics and imaging have led to an explosion of molecular and cellular profiling data from large numbers of samples. This rapid increase in biological data dimension and acquisition rate is challenging conventional analysis strategies. Modern machine learning methods, such as deep learning, promise to leverage very large data sets for finding hidden structure within them, and for making accurate predictions. In this review, we discuss applications of this new breed of analysis approaches in regulatory genomics and cellular imaging. We provide background of what deep learning is, and the settings in which it can be successfully applied to derive biological insights. In addition to presenting specific applications and providing tips for practical use, we also highlight possible pitfalls and limitations to guide computational biologists when and how to make the most use of this new technology.

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  • Massively parallel reporter assays coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches.

    “Massively parallel reporter assays (MPRAs) – methods that assess thousands to millions of sequence variants in a single experiment – coupled with machine learning have proven useful in addressing similar voids by producing quantitative biological insight that would be difficult to achieve through traditional approaches3–9.”

    From Human 5′ UTR design and variant effect prediction from a massively parallel translation assay (2019) · cites this paper as [3]

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Cited by

Sorted by citations

Authors
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O.
Affiliations
  • European Bioinformatics Institute (GB)
  • Wellcome Sanger Institute (GB)
Corresponding author
  • Parts, Leopold
  • Stegle, Oliver
ORCID
Venue
Molecular Systems Biology
Publisher
Springer Science and Business Media LLC
Published
2016-07-01
Volume
12
Issue
7
Pages
878-878
ISSN
1744-4292 (ISSN-L)
DOI
10.15252/msb.20156651
PMC
PMC4965871
OpenAlex
W2502949459
Cited by
1,473
References
27
Citations by year
2026: 94 · 2025: 159 · 2024: 140 · 2023: 133 · 2022: 133 · 2021: 170 · 2020: 215 · 2019: 197 · 2018: 154 · 2017: 67 · 2016: 9 · 2013: 1
FWCI
12.90
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Systems Biology (publishedVersion, other-oa)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • European Molecular Biology Organization
  • European Commission
  • Estonian Research Council (IUT34‐4)
  • Wellcome Trust
  • European Research Council (N635290)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
deep learning, computational biology, regulatory genomics, cell imaging, high-dimensional biological data, machine learning, deep learning limitations
MeSH terms
Machine Learning, Humans, Models, Genetic, Computational Biology, Genomics

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