ArticleScientific reports2022
A deep learning model to classify neoplastic state and tissue origin from transcriptomic data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 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
- Cancer of unknown primary: the evolution of tissue of origin identification in the artificial intelligence era.Biomarker research · 2026Review
- Deep Learning Approach for Biomedical Image Classification.Journal of imaging informatics in medicine · 2026Review
- Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.Nature reviews. Clinical oncology · 2025Review
- Learning the cellular origins across cancers using single-cell chromatin landscapes.Nature communications · 2025Article
- IC2Bert: masked gene expression pretraining and supervised fine tuning for robust immune checkpoint blockade (ICB) response prediction.Scientific reports · 2025Article
- A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data.PLoS computational biology · 2024Article
- The practical utility of AI-assisted molecular profiling in the diagnosis and management of cancer of unknown primary: an updated review.Virchows Archiv : an international journal of pathology · 2024Review
- Multi-omics based artificial intelligence for cancer research.Advances in cancer research · 2024Review
- Machine learning for pan-cancer classification based on RNA sequencing data.Frontiers in molecular biosciences · 2023Review
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Authors and funding
3 authors.
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Abstract
Application of deep learning methods to transcriptomic data has the potential to enhance the accuracy and efficiency of tissue classification and cell state identification. Herein, we developed a multitask deep learning model for tissue classification combining publicly available whole transcriptomic (RNA-seq) datasets of non-neoplastic, neoplastic and peri-neoplastic tissue to classify disease state, tissue origin and neoplastic subclass. RNA-seq data from a total of 10,116 patient samples processed through a common pipeline were used for model training and validation. The model achieved 99% accuracy for disease state classification (ROC-AUC of 0.98) and 97% accuracy for tissue origin (ROC-AUC of 0.99). Moreover, the model achieved an accuracy of 92% (ROC-AUC 0.95) for neoplastic subclassification. This is the first multitask deep learning algorithm developed for tissue classification employing a uniform pipeline analysis of transcriptomic data with multiple tissue classifiers. This model serves as a framework for incorporating large transcriptomic datasets across conditions to facilitate clinical diagnosis and cell-based treatment strategies.
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