Hybrid GNN-RNN Model for Cardiomyocyte Differentiation Prediction

This contains datasets used in the development of a hybrid GNN-RNN framework for cardiomyocyte differentiation
A state-of-the-art deep learning framework that combines Graph Neural Networks (GNN) and Recurrent Neural Networks (RNN) for predicting cardiomyocyte differentiation trajectories. This project integrates spatial multi-omics data (spatial transcriptomics) with temporal gene expression patterns to achieve superior classification performance and biological interpretability(96.67% accuracy).
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