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DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome
Behrooz Azarkhalili1,2, Ali Saberi3, Hamidreza Chitsaz4
1Department of Stem Cell Biology and Technology, Royan Institute, Tehran, Iran.
Scientific Reports
|November 13, 2019
Summary
This study introduces a novel deep neural network (DNN) for cancer pathology, encoding whole transcriptomes into a low-dimensional vector for accurate sample classification. The AI model achieves high accuracy in identifying tissue and cancer types, outperforming existing methods.
Area of Science:
- Computational Biology
- Bioinformatics
- Artificial Intelligence in Oncology
Background:
- Molecular cancer pathology often relies on limited biomarkers due to computational challenges with whole transcriptome analysis.
- Existing methods struggle to efficiently process and interpret complex genomic data for cancer subtyping.
Purpose of the Study:
- To develop a novel Deep Neural Network (DNN) architecture for comprehensive molecular cancer pathology.
- To enable simultaneous inference of biological sample properties, including tissue and disease type, from whole transcriptome data.
- To overcome computational limitations in analyzing large-scale transcriptomic datasets.
Main Methods:
- Implemented a multi-task and transfer learning DNN architecture.
- Encoded whole transcription profiles into an 8-dimensional latent vector.
- Utilized mRNA transcription profiles from 10,750 clinical samples across 34 classes and 27 tissues.
Main Results:
- The DNN achieved superior performance in predicting tissue-of-origin, normal vs. disease state, and cancer type.
- Achieved 99.4% accuracy in identifying cancer subtypes within specific tissues.
- Demonstrated robustness against noise and missing data in transcriptomic profiles.
- The low-dimensional latent space significantly improved sample discrimination compared to original gene expression profiles.
Conclusions:
- The developed AI system, DeePathology, offers a powerful tool for molecular cancer pathology and oncological research.
- Highlights the potential of artificial intelligence to address computational challenges in analyzing complex biological data.
- Provides a robust and accurate method for cancer classification and subtype identification.
