Genomics Inform.  2019 Sep;17(3):e30. 10.5808/GI.2019.17.3.e30.

Deep learning for stage prediction in neuroblastoma using gene expression data

Affiliations
  • 1Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21565, Korea. nams@gachon.ac.kr
  • 2Department of Genome Medicine and Science, College of Medicine, Gachon University, Incheon 21565, Korea.
  • 3Department of Life Sciences, Gachon University, Seongnam 13120, Korea.
  • 4Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon 21565, Korea.

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.

Keyword

deep learning; gene expression; neuroblastoma

MeSH Terms

Classification
Dataset
Diagnosis
Gene Expression*
Humans
Learning*
Neuroblastoma*
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