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Intelligent phenotype-detection and gene expression profile generation with generative adversarial networks
Hamid Ravaee1, Mohammad Hossein Manshaei1, Mehran Safayani1
1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Journal of Theoretical Biology
|November 9, 2023
Summary
This study introduces Intelligent Phenotype-detection and Gene expression profile Generation (IP3G), a Generative Adversarial Network model. IP3G enhances gene expression data and discovers cancer phenotypes without labels, improving classification accuracy.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Gene expression analysis is crucial for cancer classification and phenotype identification.
- High-throughput RNA sequencing generates vast datasets, but faces data security and privacy challenges for machine learning models.
- Developing robust machine learning classifiers requires addressing data augmentation and unsupervised phenotype discovery.
Purpose of the Study:
- To propose IP3G (Intelligent Phenotype-detection and Gene expression profile Generation), a Generative Adversarial Network-based model.
- To tackle gene expression data augmentation and unsupervised phenotype discovery challenges.
- To enhance cancer type classification and identify diverse cancer phenotypes.
Main Methods:
- Developed IP3G, a Generative Adversarial Network model for gene expression data.
- Converted gene expression profiles into 2-Dimensional images for phenotype generation.
- Improved the Generative Adversarial Network objective function using Earth Mover Distance and a novel mutual information function.
- Utilized disentangled representation learning for unsupervised phenotype identification.
Main Results:
- IP3G successfully augments gene expression data and discovers phenotypes without labeled data.
- The model outperforms traditional clustering methods (k-Means, DBSCAN, GMM) in unsupervised phenotype discovery.
- IP3G surpasses Support Vector Machine (SVM) and Convolutional Neural Network (CNN) classification accuracy by up to 6% through data augmentation.
- Source code is publicly available on GitHub.
Conclusions:
- IP3G offers a novel solution for gene expression data augmentation and unsupervised phenotype discovery in cancer research.
- The model effectively addresses data security and privacy concerns by generating synthetic data.
- IP3G demonstrates significant improvements in both phenotype discovery and cancer classification accuracy.
Keywords:
Augmentation of RNA-seq dataCancer diagnosisCancer phenotype detectionGene expressionGenerative adversarial networksMore Related Videos
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