📋 Enhancer-GAN: High-Activity Enhancer Generation based on Feedback GAN with Domain Constraint and Curriculum Learning
Here, we propose an AI-driven enhancer design method, named Enhancer-GAN, to generate high-activity enhancer sequences. Enhancer-GAN is firstly pre-trained on a large enhancer dataset that contains both low-activity and high-activity enhancers, and then is optimized to generate high-activity enhancers with feedback-loop mechanism. Domain constraint and curriculum learning were introduced into Enhancer-GAN to alleviate the noise from feedback loop and accelerate the training convergence. Experimental results on benchmark datasets demonstrate that the activity of generated enhancers is significantly higher than ones in benchmark dataset. Besides, we find 10 new motifs from generated high-activity enhancers. These results demonstrate Enhancer-GAN is promising to generate and optimize bio-sequences with desired properties.