Analyzing RNA-Seq gene expression data using deep learning approaches for cancer classification

• Main Authors: Laiqa Rukhsar, Waqas Haider Bangyal, Muhammad Sadiq Ali Khan, Ag Asri Ag Ibrahim, Kashif Nisar, Danda B. Rawat
• Format: Article
• Language: English; English
• Published: MDPI AG, Basel, Switzerland 2022
• Subjects: QP1-(981) Physiology; RC254-282 Neoplasms. Tumors. Oncology Including cancer and carcinogens
• 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
• Institution: Universiti Malaysia Sabah

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...