ArticleFrontiers in bioengineering and biotechnology2020
Pan-Cancer Classification Based on Self-Normalizing Neural Networks and Feature Selection.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine Learning-Based Identification of Candidate Serum miRNA Features for Pan-Cancer and Cancer Type Classification.Life (Basel, Switzerland) · 2026Article
- Harnessing interpretable deep learning to predict resistance inFrontiers in cellular and infection microbiology · 2026Article
- Unveiling Epigenetic Regulatory Elements Associated with Breast Cancer Development.International journal of molecular sciences · 2025Article
- Computational models for pan-cancer classification based on multi-omics data.Frontiers in genetics · 2025Review
- Article
- Identification of mClinical epigenetics · 2023Article
- Exploring Histological Similarities Across Cancers From a Deep Learning Perspective.Frontiers in oncology · 2022Article
- Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.Computational intelligence and neuroscience · 2022Article
- Article
- Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data.Scientific reports · 2021Article
- Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure.Frontiers in physiology · 2020Article
- Identification of Common Genes and Pathways in Eight Fibrosis Diseases.Frontiers in genetics · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
Cancer is a one of the severest diseases and cancer classification plays an important role in cancer diagnosis and treatment. Some different cancers even have similar molecular features such as DNA copy number variant. Pan-cancer classification is still non-trivial at molecular level. Herein, we propose a computational method to classify cancer types by using the self-normalizing neural network (SNN) for analyzing pan-cancer copy number variation data. Since the dimension of the copy number variation features is high, the Monte Carlo feature selection method was used to rank these features. Then a classifier was built by SNN and feature selection method to select features. Three thousand six hundred ninety-four features were chosen for the prediction model, which yields the accuracy value is 0.798 and macro F1 is 0.789. We compared our model to random forest method. Results show the accuracy and macro F1 obtained by our classifier are higher than those obtained by random forest classifier, indicating the good predictive power of our method in distinguishing four different cancer types. This method is also extendable to pan-cancer classification for other molecular features.
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