Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey
Variational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and l...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9311619/ |
| _version_ | 1819180424206745600 |
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| author | Ruoqi Wei Ausif Mahmood |
| author_facet | Ruoqi Wei Ausif Mahmood |
| author_sort | Ruoqi Wei |
| collection | DOAJ |
| description | Variational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and large-scale biological sequence analyses, among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution. The ability of VAEs to synthesize new data with more representation variance at state-of-art levels provides hope that the chronic scarcity of labeled data in the biomedical field can be resolved. Furthermore, VAEs have made nonlinear latent variable models tractable for modeling complex distributions. This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning. In this article, we review the various recent advancements in the development and application of VAEs for biomedical informatics. We discuss challenges and future opportunities for biomedical research with respect to VAEs. |
| first_indexed | 2024-12-22T22:14:07Z |
| format | Article |
| id | doaj.art-8adda272776a443b9b2c0bdbab2515cc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T22:14:07Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8adda272776a443b9b2c0bdbab2515cc2022-12-21T18:10:48ZengIEEEIEEE Access2169-35362021-01-0194939495610.1109/ACCESS.2020.30483099311619Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A SurveyRuoqi Wei0https://orcid.org/0000-0002-1771-542XAusif Mahmood1https://orcid.org/0000-0002-8991-4268Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, USADepartment of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, USAVariational autoencoders (VAEs) are deep latent space generative models that have been immensely successful in multiple exciting applications in biomedical informatics such as molecular design, protein design, medical image classification and segmentation, integrated multi-omics data analyses, and large-scale biological sequence analyses, among others. The fundamental idea in VAEs is to learn the distribution of data in such a way that new meaningful data with more intra-class variations can be generated from the encoded distribution. The ability of VAEs to synthesize new data with more representation variance at state-of-art levels provides hope that the chronic scarcity of labeled data in the biomedical field can be resolved. Furthermore, VAEs have made nonlinear latent variable models tractable for modeling complex distributions. This has allowed for efficient extraction of relevant biomedical information from learned features for biological data sets, referred to as unsupervised feature representation learning. In this article, we review the various recent advancements in the development and application of VAEs for biomedical informatics. We discuss challenges and future opportunities for biomedical research with respect to VAEs.https://ieeexplore.ieee.org/document/9311619/Deep learningvariational autoencoders (VAEs)data representationgenerative modelsunsupervised learningrepresentation learning |
| spellingShingle | Ruoqi Wei Ausif Mahmood Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey IEEE Access Deep learning variational autoencoders (VAEs) data representation generative models unsupervised learning representation learning |
| title | Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey |
| title_full | Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey |
| title_fullStr | Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey |
| title_full_unstemmed | Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey |
| title_short | Recent Advances in Variational Autoencoders With Representation Learning for Biomedical Informatics: A Survey |
| title_sort | recent advances in variational autoencoders with representation learning for biomedical informatics a survey |
| topic | Deep learning variational autoencoders (VAEs) data representation generative models unsupervised learning representation learning |
| url | https://ieeexplore.ieee.org/document/9311619/ |
| work_keys_str_mv | AT ruoqiwei recentadvancesinvariationalautoencoderswithrepresentationlearningforbiomedicalinformaticsasurvey AT ausifmahmood recentadvancesinvariationalautoencoderswithrepresentationlearningforbiomedicalinformaticsasurvey |