ArticleGenomics & informatics2019
Deep learning for stage prediction in neuroblastoma using gene expression data.
Article in Genomics & informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Deep Learning-Based Analysis of Gene Expression Data and Gene-Related Information in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2026Article
- Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research.Molecular neurobiology · 2025Review
- A multi-omics approach for biomarker discovery in neuroblastoma: a network-based framework.NPJ systems biology and applications · 2024Article
- Predicting Neuroblastoma Patient Risk Groups, Outcomes, and Treatment Response Using Machine Learning Methods: A Review.Medical sciences (Basel, Switzerland) · 2024Review
- Joint triplet loss with semi-hard constraint for data augmentation and disease prediction using gene expression data.Scientific reports · 2023Article
- Neuroblastoma GD2 Expression and Computational Analysis of Aptamer-Based Bioaffinity Targeting.International journal of molecular sciences · 2021Review
- A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma.Frontiers in oncology · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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.
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Registered trials
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