Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine learning for pan-cancer classification based on RNA sequencing data

10 Nov 2023

Abstract excerpt

Despite recent improvements in cancer diagnostics, 2%-5% of all malignancies are still cancers of unknown primary (CUP), for which the tissue-of-origin (TOO) cannot be determined at the time of presentation. Since the primary site of cancer leads to the choice of optimal treatment, CUP patients pose a significant clinical challenge with limited treatment options. Data produced by large-scale cancer genomics...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Diagnosis and Treatment
Share to Cancer Genomics and Diagnostics
Share to Head and Neck Cancer Studies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 38028533
DOI: 10.3389/fmolb.2023.1285795

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning for pan-cancer classification based on RNA sequencing dataPMID 38028533Read ↗

A computational framework to trace tumor tissue-of-origin of 19 cancer types based on RNA sequencingSemantic proximity Read ↗

Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor typeSemantic proximity Read ↗

RNA-Seq Accurately Identifies Cancer Biomarker Signatures to Distinguish Tissue of OriginSemantic proximity Read ↗

A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencingSemantic proximity Read ↗

Application of Transcriptome–Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic CancerSemantic proximity Read ↗

A cross-cohort computational framework to trace tumor tissue-of-origin based on RNA sequencingSemantic proximity Read ↗

A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation.Semantic proximity Read ↗

A Neural Network Framework for Predicting the Tissue-of-Origin of 15 Common Cancer Types Based on RNA-Seq DataSemantic proximity Read ↗

Validation of a transcriptome-based assay for classifying cancers of unknown primary originSemantic proximity Read ↗

TRANSCUP: a scalable workflow for predicting cancer of unknown primary based on next-generation transcriptome profilingSemantic proximity Read ↗

Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review.Semantic proximity Read ↗

Application of a Neural Network Whole Transcriptome–Based Pan-Cancer Method for Diagnosis of Primary and Metastatic CancersSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.