GeneCT: a generalizable cancerous status and tissue origin classifier for pan-cancer biopsies
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Summary
A deep learning-based classifier, named GeneCT, for predicting cancerous status and tissue origin of pan-cancer biopsies and it is believed that GeneCT can potentially facilitate cancer diagnosis, tumor origin determination and molecular cancer studies.
- Type
- article
- Published
- 2018-06-27
- Cited by
- 17
- References
- 12
- Access
- Open access
- OpenAlex
- https://openalex.org/W2809879579
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:49431564
Keywords
Classifier (UML), Perl, Cancer, Computer science, Source code
References
- Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types
- The Cancer Genome Atlas Pan-Cancer Analysis Project
- RNA-Seq Accurately Identifies Cancer Biomarker Signatures to Distinguish Tissue of Origin
- A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control consortium
- Evaluation of data discretization methods to derive platform independent isoform expression signatures for multi-class tumor subtyping
- Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes
- Pan-cancer transcriptome analysis reveals a gene expression signature for the identification of tumor tissue origin
- Dermatologist–level classification of skin cancer with deep neural networks
- A comprehensive genomic pan-cancer classification using The Cancer Genome Atlas gene expression data
- An introduction to deep learning on biological sequence data: examples and solutions
- Tumor origin detection with tissue‐specific miRNA and DNA methylation markers
- Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Cited by
- Epigenetic Biomarkers in Cell-Free DNA and Applications in Liquid Biopsy
- Convolutional neural network models for cancer type prediction based on gene expression
- Bioinformatics Analysis for Circulating Cell-Free DNA in Cancer
- Liquid Biopsy of Methylation Biomarkers in Cell-Free DNA.
- Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis
- An Ensemble Model for Tumor Type Identification and Cancer Origins Classification
- Diagnostic and Therapeutic Potential of Circulating-Free DNA and Cell-Free RNA in Cancer Management
- Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests
- Plasmonic Biosensor Based on Ag-TiO2-ZnO Gratings for Cancer Detection in the Optical Communication Band
- Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study
- GENESO: A Framework for Pan-Cancer Classification and Marker Gene Discovery by Symmetrical Occlusion Method Using Deep Learning
- Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology
- Occlusion enhanced pan-cancer classification via deep learning
- IMPRESS: Improved methylation profiling using restriction enzymes and smMIP sequencing, combined with a new biomarker panel, creating a multi-cancer detection assay
- The 100 most-cited radiomics articles in cancer research: A bibliometric analysis.
- Computational models for pan-cancer classification based on multi-omics data
- Additional file 1 of Occlusion enhanced pan-cancer classification via deep learning
- LSTM neural network for pan-cancer classification & marker gene discovery by symmetrical occlusion method
- Additional file 3 of Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology
- Additional file 1 of Occlusion enhanced pan-cancer classification via deep learning