Synthetic data used in the paper 'DART: Deep learning for the Analysis and Reconstruction of Transcriptional dynamics from live-cell imaging data'

This dataset contains the synthetic data used in the paper:DART: Deep learning for the Analysis and Reconstruction of Transcriptional dynamics from live-cell imaging data (Ma & Grima, 2025). The dataset includes: Idealized synthetic data at three burstiness levels Realistic synthetic data at three burstiness levels with added noise (5%, 10%, 20%) Trained DART models and binarized promoter states Comparison results with other binarization methods Synthetic data from multi-state promoter-switching models used for SVM classification and model selection results For a detailed description of the folder structure and usage instructions, please refer to the GitHub repository:https://github.com/mmmuhan/DART