Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective
Abstract The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. How...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-09-01
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| Series: | Computational and Systems Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/cso2.1050 |
| _version_ | 1797672595543293952 |
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| author | Xusheng Ai Melissa C Smith Frank Alex Feltus |
| author_facet | Xusheng Ai Melissa C Smith Frank Alex Feltus |
| author_sort | Xusheng Ai |
| collection | DOAJ |
| description | Abstract The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA‐seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA‐seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio‐informatics. Finally, we propose potential directions for future research. |
| first_indexed | 2024-03-11T21:33:27Z |
| format | Article |
| id | doaj.art-00df1bc4c4d444e89b2ddb0d331aab78 |
| institution | Directory Open Access Journal |
| issn | 2689-9655 |
| language | English |
| last_indexed | 2024-03-11T21:33:27Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Computational and Systems Oncology |
| spelling | doaj.art-00df1bc4c4d444e89b2ddb0d331aab782023-09-27T07:02:29ZengWileyComputational and Systems Oncology2689-96552023-09-0133n/an/a10.1002/cso2.1050Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspectiveXusheng Ai0Melissa C Smith1Frank Alex Feltus2Electrical and Computer Engineering Department Clemson University ClemsonSCUSAElectrical and Computer Engineering Department Clemson University ClemsonSCUSADepartment of Genetics and Biochemistry, Biomedical Data Science & Informatics Program, Clemson Center for Human Genetics Clemson University ClemsonSCUSAAbstract The remarkable flexibility and adaptability of generative adversarial networks (GANs) have led to the proliferation of its models in bioinformatics research. Proteomic and transcriptomic profiles have been shown to be promising methods for discovering and identifying disease biomarkers. However, those analyses were performed by trained human examiners making the process tedious, time consuming, and hard to standardize. With the development of GANs, it is now possible to reduce computational costs and human time for bioinformatics analysis to produce effective biomarkers. Moreover, GANs help address the lack of phenotypic state transitional gene expression data as well as avoid protected human data constraints by generating RNA sequencing (RNA‐seq) data from random vectors. The purpose of this review is to summarize the use of GAN approaches and techniques to augment RNA‐seq expression data and identify clinically useful biomarkers. We compare different studies that use different types of GAN models to examine the biomarkers. Also, we identify research gaps and challenges that apply GANs to bio‐informatics. Finally, we propose potential directions for future research.https://doi.org/10.1002/cso2.1050biomarkersdata augmentationgenerative adversarial networkRNA sequencing |
| spellingShingle | Xusheng Ai Melissa C Smith Frank Alex Feltus Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective Computational and Systems Oncology biomarkers data augmentation generative adversarial network RNA sequencing |
| title | Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective |
| title_full | Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective |
| title_fullStr | Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective |
| title_full_unstemmed | Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective |
| title_short | Generative adversarial networks applied to gene expression analysis: An interdisciplinary perspective |
| title_sort | generative adversarial networks applied to gene expression analysis an interdisciplinary perspective |
| topic | biomarkers data augmentation generative adversarial network RNA sequencing |
| url | https://doi.org/10.1002/cso2.1050 |
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