You Only Train Once: Differentiable Subset Selection for Omics Data

1. 1Implement YOTO framework using Python
2. 2Load single-cell transcriptomic data into YOTO
3. 3Configure hyperparameters for subset selection
4. 4Train YOTO model using the loaded data
5. 5Evaluate the performance of the selected subset

Data scientists and bioinformaticians on a team can benefit from YOTO as it streamlines the feature selection process, making it more efficient and effective. This can lead to better biomarker discovery and more accurate predictions

💡 YOTO enables end-to-end learning for subset selection, making it a powerful tool for biomarker discovery and interpretability

🧬 YOTO: an end-to-end framework for differentiable subset selection in omics data! 💡