ArticleScientific reports2019
DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Non-invasive quantification of viability in liver spheroids using deep learning.Frontiers in bioengineering and biotechnology · 2026Article
- Metabolic reprogramming and functional crosstalk within the tumor microenvironment (TME) and A Multi-omics anticancer approach.Medical oncology (Northwood, London, England) · 2025Review
- A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.Cell reports. Medicine · 2024Article
- Spatial mapping of the DNA adducts in cancer.DNA repair · 2023Review
- DeeP4med: deep learning for P4 medicine to predict normal and cancer transcriptome in multiple human tissues.BMC bioinformatics · 2023Article
- The effect of non-linear signal in classification problems using gene expression.PLoS computational biology · 2023Article
- Machine learning for pan-cancer classification based on RNA sequencing data.Frontiers in molecular biosciences · 2023Review
- A deep learning model to classify neoplastic state and tissue origin from transcriptomic data.Scientific reports · 2022Article
- Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes.Biology · 2022Article
- InnovativeFrontiers in genetics · 2022Review
- XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.Briefings in bioinformatics · 2021Article
- Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions.International journal of molecular sciences · 2021Review
- Article
- Discovery of primary prostate cancer biomarkers using cross cancer learning.Scientific reports · 2021Article
- Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.International journal of molecular sciences · 2020Article
- Deep learning models in genomics; are we there yet?Computational and structural biotechnology journal · 2020Review
- The assessment of efficient representation of drug features using deep learning for drug repositioning.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Networks (DNNs) that is capable of simultaneous inference of various properties of biological samples, through multi-task and transfer learning. It encodes the whole transcription profile into a strikingly low-dimensional latent vector of size 8, and then recovers mRNA and miRNA expression profiles, tissue and disease type from this vector. This latent space is significantly better than the original gene expression profiles for discriminating samples based on their tissue and disease. We employed this architecture on mRNA transcription profiles of 10750 clinical samples from 34 classes (one healthy and 33 different types of cancer) from 27 tissues. Our method significantly outperforms prior works and classical machine learning approaches in predicting tissue-of-origin, normal or disease state and cancer type of each sample. For tissues with more than one type of cancer, it reaches 99.4% accuracy in identifying the correct cancer subtype. We also show this system is very robust against noise and missing values. Collectively, our results highlight applications of artificial intelligence in molecular cancer pathology and oncological research. DeePathology is freely available at https://github.com/SharifBioinf/DeePathology .
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