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Cited by 96Gene expression and cancer classificationDomain Adaptation and Few-Shot LearningBreast Cancer Treatment Studies

A meta-learning approach for genomic survival analysis

Open access
Qiu, Yeping Lina, Zheng, Hong, Devos, Arnout, Selby, Heather, Gevaert, Olivier·2020·Nature Communications
DOI 10.1038/s41467-020-20167-3PMC PMC7733508

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Abstract

RNA sequencing has emerged as a promising approach in cancer prognosis as sequencing data becomes more easily and affordably accessible. However, it remains challenging to build good predictive models especially when the sample size is limited and the number of features is high, which is a common situation in biomedical settings. To address these limitations, we propose a meta-learning framework based on neural networks for survival analysis and evaluate it in a genomic cancer research setting. We demonstrate that, compared to regular transfer-learning, meta-learning is a significantly more effective paradigm to leverage high-dimensional data that is relevant but not directly related to the problem of interest. Specifically, meta-learning explicitly constructs a model, from abundant data of relevant tasks, to learn a new task with few samples effectively. For the application of predicting cancer survival outcome, we also show that the meta-learning framework with a few samples is able to achieve competitive performance with learning from scratch with a significantly larger number of samples. Finally, we demonstrate that the meta-learning model implicitly prioritizes genes based on their contribution to survival prediction and allows us to identify important pathways in cancer.

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  • Qiu et al. introduced a meta-learning framework to identify sample representation by integrating trained models across multiple tasks and performing prognosis prediction with limited training data.

    “For instance, Qiu et al. introduced a meta-learning framework to identify the sample representation by integrating the trained models across multiple tasks and performing prognosis prediction with limited training data [182].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [182]

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Research works printed but not linked: 11

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  11. [62]Qiu, Y. L., Zheng, H., Devos, A., Selby, H. & Gevaert, O. Supporting software metalearning_survival. A meta-learning approach for genomic survival analysis. 10.5281/zenodo.4116296 (2020). PMC7733508 33311484 · DOI

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Authors
Qiu, Y. L., Zheng, H., Devos, A., Selby, H., & Gevaert, O.
Affiliations
  • Stanford Medicine (US)
  • École Polytechnique Fédérale de Lausanne (CH)
Corresponding author
Gevaert, Olivier
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2020-12-11
Volume
11
Issue
1
Pages
6350-6350
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-020-20167-3
PMC
PMC7733508
OpenAlex
W3112653481
Cited by
96
References
51
Citations by year
2026: 15 · 2025: 18 · 2024: 25 · 2023: 17 · 2022: 11 · 2021: 9 · 2020: 1
FWCI
3.08
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • European Commission (HORIZON2020, 754354)
  • National Cancer Institute (R01CA244550)
  • National Institute of Biomedical Imaging and Bioengineering (R01EB020527, R56EB020527)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
Keywords
meta-learning, survival analysis, cancer prognosis, RNA sequencing, high-dimensional data, transfer learning, small sample size, gene prioritization, pathway identification, artificial neural networks
MeSH terms
Machine Learning, Algorithms, Humans, Neoplasms, Prognosis, Survival Analysis, Neural Networks, Computer, Computational Biology, Genomics

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