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This repository includes 1. code for offspring GAN 2. code for evaluation of synthetic data from offspring GAN 3. PCA plots for synthetic data generated by 3 diffferent GAN models.

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Offspring_GAN

This repository includes 1. code for offspring GAN & PPO-GAN 2. code for evaluation of synthetic data from offspring GAN 3. PCA plots for synthetic data generated by 3 diffferent GAN models. 3. code for requesting GPU node for interactive use.

Abstract

Genomics data have been used for disease phenotype prediction in precision medicine for a long time. In recent years, many such prediction models are built using machine Learning (ML) algorithms. As of today, Genomic data and other biomedical data suffers from sampling bias in terms of peoples’ ethnicity as most data comes from people of European ancestry. A smaller sample size for other population groups can cause suboptimal results in ML-based prediction models for those populations. Suboptimal predictions in precision medicine for some particular group can cause serious consequences limiting models applicability in real world problems. As data collection for those populations is time-consuming and costly, we suggest Deep Learning-based models for in-silico data enhancement. Existing GAN models for genomic data like Popu- lation scale Genomic conditional-GAN (PG-cGAN) can generate realistic genomic data while trained on fairly unbiased data, but fails while trained on biased data and encounters severe mode collapse. Our proposed model Offspring- Generative Adversarial Network (Offspring GAN) can resolve the mode collapse issue even when trained in strongly biased genomic datasets.We compare synthetic data generated by both of the model to benchmark our model with pre-existing PG-cGAN model. Our results show the ability of Off- spring GAN to generate aware realistic and diverse data which can be augmented with real data to alleviate the data bias. We also pro- pose a privacy-preserving protocol using Offspring GAN to protect the privacy of real data for respective lab.

Offspring GAN architecture

Real data is passed through the Mendelian hybridizer, and it generates genotypes offspring generation, which is passed to the discriminator as real data. On the other hand, the generator takes an input of a conditional label (which determines the ethnicity) and a noise vector and gener- ates a synthetic genotype for the respective ethnicity. After training generator is capable of label-aware realistic data generation, which is passed to a second Mendelian hybridizer, and it can produce 𝐹1 generation genotype of synthetic data with a lower computational cost than the generator itself.

Implications

  1. Data augmentation on heavily biased genomic dataset
  2. Privacy Preserving protocol using Offspring genotype

Getting started

Codes are provided in ipython notebook files with instruction added in markdown text.

  1. One can get started by downloading the same and running in google collab.
  2. For running in local devices make sure your vertual environment satisfies following dependencies :Tensorflow : 2.x, Numpy : 1.19.x, Pandas : 1.1.5, SkLearn : 1.0, plotly : 5.8, mathplotlib : 3.5.x

For running on HPC in interactive manner one can request GPU node as given in HPC_GPU.sh file in code folder.

Citation:

Supratim Das and Xinghua Shi. 2022. Offspring GAN augments biased human genomic data. In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics (BCB '22). Association for Computing Machinery, New York, NY, USA, Article 50, 1–10. https://doi.org/10.1145/3535508.3545537

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This repository includes 1. code for offspring GAN 2. code for evaluation of synthetic data from offspring GAN 3. PCA plots for synthetic data generated by 3 diffferent GAN models.

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