Jun 30, 2023 · This article comprehensively reviews and evaluates GAN-based generative models on the TCGA dataset in view of achieving data augmentation and enabling deep learning applications to …

Jun 30, 2023 · In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of cancer phenotypes. Results: This work highlights a …

Jun 30, 2023 · In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of cancer phenotypes.

In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of can-cer phenotypes.

Jun 1, 2023 · In this article, we develop a method based on a conditional generative adversarial network to generate realistic transcriptomics data for Escherichia coli and humans.

Bibliographic details on GAN-based data augmentation for transcriptomics: survey and comparative assessment.

GAN-based data augmentation for transcriptomics: survey and comparative assessment. 31st Intelligent Systems for Molecular Biology (ISMB 2023), Jul 2023, Lyon, France. pp.i111-i120, …

The article reviews and evaluates GAN - based generative models for data augmentation in transcriptomics. Performance indicators capture different aspects of generated data.

In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of cancer phenotypes. Results This work highlights a significant …

Our evaluation opens new perspectives for GAN-based data augmentation, balancing a priori the diverse modes of the real data and aligning a posteriori the generated data.