Abstract: The present invention relates to systems and methods for automated interpretable and generalizable biological morphological profiling. The method for identifying single-cell morphological profiling based on deep learning includes collecting and pre-processing at least one single-cell image data; training Variational Autoencoder (VAE) by defining an arbitrary dimension size of a latent space; distilling a learnt latent space from the VAE to Generative Adversarial Network (GAN) and training a generator-discriminator combination within the GAN; generating a realistic image aligned with the learnt latent space; and interpreting data by incorporating statistical variance analysis and hierarchical clustering.