ArticleFrontiers in bioengineering and biotechnology2020
A Deep Learning Framework to Predict Tumor Tissue-of-Origin Based on Copy Number Alteration.
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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.Briefings in bioinformatics · 2024Pooled it
- Primary thoracic synovial sarcoma in a 45-year-old male: A case report and literature review.Oncology letters · 2026Article
- Soft multiclass feature augmented deep learning to predict tumor origins using cytology or histology whole slide images.NPJ digital medicine · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling.Cancer research communications · 2025Article
- A platform-independent AI tumor lineage and site (ATLAS) classifier.Communications biology · 2024Article
- Imaging features and deep learning for prediction of pulmonary epithelioid hemangioendothelioma in CT images.Journal of thoracic disease · 2024Article
- Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis.Frontiers in oncology · 2023Article
- A Novel Algorithm for Detecting Microsatellite Instability Based on Next-Generation Sequencing Data.Frontiers in oncology · 2022Article
- CRIA: An Interactive Gene Selection Algorithm for Cancers Prediction Based on Copy Number Variations.Frontiers in plant science · 2022Article
- Informative SNP Selection Based on a Fuzzy Clustering and Improved Binary Particle Swarm Optimization Algorithm.Computational and mathematical methods in medicine · 2022Article
- Scalable analysis of multi-modal biomedical data.GigaScience · 2021Article
- HFS-SLPEE: A Novel Hierarchical Feature Selection and Second Learning Probability Error Ensemble Model for Precision Cancer Diagnosis.Frontiers in cell and developmental biology · 2021Article
- Application of Circulating Tumor DNA as a Biomarker for Non-Small Cell Lung Cancer.Frontiers in oncology · 2021Review
- Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study.Frontiers in microbiology · 2021Article
- PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm.Frontiers in genetics · 2021Review
- Evaluating the Risk of Breast Cancer Recurrence and Metastasis After Adjuvant Tamoxifen Therapy by Integrating Polymorphisms in Cytochrome P450 Genes and Clinicopathological Characteristics.Frontiers in oncology · 2021Article
- Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes.Frontiers in genetics · 2021Article
- Evaluating DNA Methylation, Gene Expression, Somatic Mutation, and Their Combinations in Inferring Tumor Tissue-of-Origin.Frontiers in cell and developmental biology · 2021Article
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9 authors.
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Abstract
Cancer of unknown primary site (CUPS) is a type of metastatic tumor for which the sites of tumor origin cannot be determined. Precise diagnosis of the tissue origin for metastatic CUPS is crucial for developing treatment schemes to improve patient prognosis. Recently, there have been many studies using various cancer biomarkers to predict the tissue-of-origin (TOO) of CUPS. However, only a very few of them use copy number alteration (CNA) to trance TOO. In this paper, a two-step computational framework called CNA_origin is introduced to predict the tissue-of-origin of a tumor from its gene CNA levels. CNA_origin set up an intellectual deep-learning network mainly composed of an autoencoder and a convolution neural network (CNN). Based on real datasets released from the public database, CNA_origin had an overall accuracy of 83.81% on 10-fold cross-validation and 79% on independent datasets for predicting tumor origin, which improved the accuracy by 7.75 and 9.72% compared with the method published in a previous paper. Our results suggested that the autoencoder model can extract key characteristics of CNA and that the CNN classifier model developed in this study can predict the origin of tumors robustly and effectively. CNA_origin was written in Python and can be downloaded from https://github.com/YingLianghnu/CNA_origin.
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