Intelligent phenotype-detection and gene expression profile generation with generative adversarial networks

Hamid Ravaee, Mohammad Hossein Manshaei, Mehran Safayani, Javad Salimi Sartakhti

Research output: Contribution to journalArticlepeer-review

Abstract

Gene expression analysis is valuable for cancer type classification and identifying diverse cancer phenotypes. The latest high-throughput RNA sequencing devices have enabled access to large volumes of gene expression data. However, we face several challenges, such as data security and privacy, when we develop machine learning-based classifiers for categorizing cancer types with these datasets. To address these issues, we propose IP3G (Intelligent Phenotype-detection and Gene expression profile Generation with Generative adversarial network), a model based on Generative Adversarial Networks. IP3G tackles two major problems: augmenting gene expression data and unsupervised phenotype discovery. By converting gene expression profiles into 2-Dimensional images and leveraging IP3G, we generate new profiles for specific phenotypes. IP3G learns disentangled representations of gene expression patterns and identifies phenotypes without labeled data. We improve the objective function of the GAN used in IP3G by employing the earth mover distance and a novel mutual information function. IP3G outperforms clustering methods like k-Means, DBSCAN, and GMM in unsupervised phenotype discovery, while also surpassing SVM and CNN classification accuracy by up to 6% through gene expression profile augmentation. The source code for the developed IP3G is accessible to the public on GitHub.

Original languageEnglish (US)
Article number111636
JournalJournal of Theoretical Biology
Volume577
DOIs
StatePublished - Jan 21 2024
Externally publishedYes

Keywords

  • Augmentation of RNA-seq data
  • Cancer diagnosis
  • Cancer phenotype detection
  • Gene expression
  • Generative adversarial networks

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics

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