ArticleBMC genomics2018
Cancer type prediction based on copy number aberration and chromatin 3D structure with convolutional neural networks.
Article in BMC genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- Machine learning prediction for AML based on 3D genome selected circRNA.NPJ systems biology and applications · 2026Article
- Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision.Current issues in molecular biology · 2024Article
- Deep learning in cancer genomics and histopathology.Genome medicine · 2024Review
- Prediction of Alzheimer's Disease Based on 3D Genome Selected circRNA.The journal of prevention of Alzheimer's disease · 2024Article
- Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study.Frontiers in big data · 2024Article
- 3D genome-selected microRNAs to improve Alzheimer's disease prediction.Frontiers in neurology · 2023Article
- BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA-miRNA interaction prediction.Interdisciplinary sciences, computational life sciences · 2022Article
- Secure tumor classification by shallow neural network using homomorphic encryption.BMC genomics · 2022Article
- Deep Learning-Based Pan-Cancer Classification Model Reveals Tissue-of-Origin Specific Gene Expression Signatures.Cancers · 2022Article
- Multiclass Cancer Prediction Based on Copy Number Variation Using Deep Learning.Computational intelligence and neuroscience · 2022Article
- Machine Learning Applicability for Classification of PAD/VCD Chemotherapy Response Using 53 Multiple Myeloma RNA Sequencing Profiles.Frontiers in oncology · 2021Article
- Pan-Cancer Genome-Wide DNA Methylation Analyses Revealed That Hypermethylation Influences 3D Architecture and Gene Expression Dysregulation in HOXA Locus During Carcinogenesis of Cancers.Frontiers in cell and developmental biology · 2021Article
- CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.EBioMedicine · 2020Article
- A novel neoantigen discovery approach based on chromatin high order conformation.BMC medical genomics · 2020Article
- Convolutional neural network models for cancer type prediction based on gene expression.BMC medical genomics · 2020Article
- A machine learning framework for the prediction of chromatin folding inPeerJ. Computer science · 2020Article
- A Shallow Convolutional Learning Network for Classification of Cancers Based on Copy Number Variations.Sensors (Basel, Switzerland) · 2019Article
- Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the developments of DNA sequencing technology, large amounts of sequencing data have been produced that provides unprecedented opportunities for advanced association studies between somatic mutations and cancer types/subtypes which further contributes to more accurate somatic mutation based cancer typing (SMCT). In existing SMCT methods however, the absence of high-level feature extraction is a major obstacle in improving the classification performance.
resultsWe propose DeepCNA, an advanced convolutional neural network (CNN) based classifier, which utilizes copy number aberrations (CNAs) and HiC data, to address this issue. DeepCNA first pre-process the CNA data by clipping, zero padding and reshaping. Then, the processed data is fed into a CNN classifier, which extracts high-level features for accurate classification. Experimental results on the COSMIC CNA dataset indicate that 2D CNN with both cell lines of HiC data lead to the best performance. We further compare DeepCNA with three widely adopted classifiers, and demonstrate that DeepCNA has at least 78% improvement of performance.
conclusionsThis paper demonstrates the advantages and potential of the proposed DeepCNA model for processing of somatic point mutation based gene data, and proposes that its usage may be extended to other complex genotype-phenotype association studies.
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