Analyzing RNA-Seq gene expression data using deep learning approaches for cancer classification
Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool with which to find underlying patterns in data, e.g., for cancer specific biomark...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
MDPI AG, Basel, Switzerland
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/32759/1/Analyzing%20RNA-Seq%20gene%20expression%20data%20using%20deep%20learning%20approaches%20for%20cancer%20classification.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/32759/2/Analyzing%20RNA-Seq%20gene%20expression%20data%20using%20deep%20learning%20approaches%20for%20cancer%20classification.pdf https://eprints.ums.edu.my/id/eprint/32759/ https://www.mdpi.com/2076-3417/12/4/1850/htm https://doi.org/10.3390/app12041850 |
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https://eprints.ums.edu.my/id/eprint/32759/1/Analyzing%20RNA-Seq%20gene%20expression%20data%20using%20deep%20learning%20approaches%20for%20cancer%20classification.ABSTRACT.pdfhttps://eprints.ums.edu.my/id/eprint/32759/2/Analyzing%20RNA-Seq%20gene%20expression%20data%20using%20deep%20learning%20approaches%20for%20cancer%20classification.pdf
https://eprints.ums.edu.my/id/eprint/32759/
https://www.mdpi.com/2076-3417/12/4/1850/htm
https://doi.org/10.3390/app12041850