A deep learning framework for hybrid heterogeneous transfer learning
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Several machine learning specialisations, including reinforcement, hybrid, multi-view and self-supervised learning, are appearing in general healthcare but have not yet gained sufficient traction in multi-omics analysis.
“Although various ML specialisations such as reinforcement (Coronato et al., 2020), hybrid (Zhou et al., 2019), multi-view (Zhao et al., 2017) and selfsupervised learning (Chen et al., 2019) are now emerging in generic health-care applications, they have not yet gained enough momentum in multi-omics analysis, hence they remain beyond the scope of this review.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Zhou et al., 2019
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Cited by
Sorted by citations
Using machine learning approaches for multi-omics data analysis: A review
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- Authors
- Zhou, J. T., Pan, S. J., & Tsang, I. W.
- Affiliations
- Institute of High Performance Computing (SG)
- Nanyang Technological University (SG)
- University of Technology Sydney (AU)
- Corresponding author
- Pan, Sinno Jialin
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 275
- Pages
- 310-328
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2950705418
- Cited by
- 53
- References
- 48
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 7 · 2023: 4 · 2022: 12 · 2021: 8 · 2020: 12 · 2019: 4
- FWCI
- 4.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 1467 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Artificial Intelligence (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- Nanyang Technological University
- Australian Research Council
- Ministry of Education - Singapore
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- heterogeneous transfer learning, feature projection, representation learning, multilingual text classification, cross-domain knowledge transfer
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