Deep learning for stage prediction in neuroblastoma using gene expression data
- Authors
- 박아론; 남승윤
- Issue Date
- Sep-2019
- Publisher
- 한국유전체학회
- Keywords
- deep learning; gene expression; neuroblastoma
- Citation
- Genomics and Informatics, v.17, no.3, pp.e30 - e30
- Journal Title
- Genomics and Informatics
- Volume
- 17
- Number
- 3
- Start Page
- e30
- End Page
- e30
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/2252
- DOI
- 10.5808/GI.2019.17.3.e30
- ISSN
- 1598-866X
- Abstract
- Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 바이오나노대학 > 생명과학과 > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.