ayanban011 / sim-gan

Missing Value Estimation of Microarray Data using Sim-GAN

Codebase for "Missing value estimation of microarray data using Sim-GAN"

Authors: Ayan Banerjee, Souemn Kumar Pati, Manan Kumar Gupta, Rinita Shai, Arijit Ghosh

Paper Link: https://link.springer.com/article/10.1007/s10115-022-01718-0

Contact: [email protected]

We are using Kent Ridge Biomedical Dataset to validate the Sim-GAN Framework Link: https://leo.ugr.es/elvira/DBCRepository/

To run the pipeline for training and evaluation on Sim-GAN framwork, simply run python3 -m main_cancer.py.

Note that any model architecture can be used as the generator and discriminator model such as multi-layer perceptrons or CNNs.

Command inputs:

• data_name: leukemia, Colon, Lung, Prostate, DLBCL
• miss_rate: probability of missing components
• batch_size: batch size
• hint_rate: hint rate
• alpha: hyperparameter
• iterations: iterations

Example command

$ python3 main_letter_spam.py --data_name leukemia 
--miss_rate: 0.2 --batch_size 128 --hint_rate 0.9 --alpha 100
--iterations 10000

Outputs

• imputed_data_x: imputed data
• rmse: Root Mean Squared Error

Acknowledgement

Our project has adapted and borrowed the code structure from GAIN.

Conclusion

Thank you and sorry for the bugs!