Deep Learning GAN Platform for Omics Data Augmentation

Overview

Developed a deep learning–based GAN methodology employing the Atlas model on high-dimensional omics datasets with limited sample sizes. This approach generated synthetic samples with far greater representativity than traditional methods (SMOTE, random oversampling), enabling class balancing and more accurate predictive modeling in biomarker discovery pipelines.

Key Features

GAN-based augmentation for transcriptomics and proteomics datasets
Deep learning architecture optimized for high-dimensional, low-sample-size data
Benchmarked against SMOTE and RO, delivering superior representativity
Integration-ready API for downstream ML workflows
Customizable for client-specific assays and rare-event classes

Add-ons

Conditional GANs for disease-specific simulations
Multi-omics augmentation (RNA-seq, proteomics, metabolomics)
Synthetic dataset annotation for training and validation
Cloud deployment for high-throughput data generation

Results

Generated 25× more usable data per assay, enabling larger training sets
Reduced costs and iteration times in assay development
Delivered predictive cell control powered by robust, AI-generated big data
AI models provided greater representativity, minimizing biases
Improved predictive accuracy and reliability of downstream outputs

Deployment
Deployed on the client’s secure internal cloud infrastructure, fully integrated into their bioinformatics workflows under GAMP-aligned validation.

Client: Mid-size pharmaceutical company (Translational Medicine R&D Unit)

Timeline: 12 months (development, benchmarking, and deployment)

Discover how Atlas can support your project

Custom Software • AI • Data Engineering • Bioinformatics

Custom Software • AI • Data Engineering • Bioinformatics

Deep Learning GAN Platform for Omics Data Augmentation

Custom Software • AI • Data Engineering • Bioinformatics

Custom Software • AI • Data Engineering • Bioinformatics

Deep Learning GAN Platform for Omics Data Augmentation

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