Publications
This work presents a large-scale protein loop modeling approach using pix2pix GAN architecture. The method enables accurate modeling of protein loop regions, which are crucial for understanding protein function and interactions.
We developed an advanced Generative Adversarial Network (GAN) architecture capable of generating tertiary protein structures that closely resemble naturally occurring proteins. The approach demonstrates significant improvements in structure quality and biological relevance.
This paper introduces the ROD-WGAN hybrid model, a generative adversarial network designed for large-scale protein tertiary structure generation. The hybrid approach combines competitive advantages of multiple GAN variants to produce high-quality protein structures at scale.
My publications focus on the application of deep learning and generative models to protein structure modeling and bioinformatics problems. Each paper represents a different aspect of my research in computational biology and machine learning for biological sequences.
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