GAN models in natural language processing and image translation

Thirumagal, E.; Saruladha, K.

doi:10.1016/b978-0-12-823519-5.00001-4

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…The GAN is a complex and significant network frequently used in many fields, such as computer vision, − natural language processing (NLP), and cybersecurity . Also, it has been used to generate protein structures that mimic the native protein structures .…”

Section: Proposed Methodologymentioning

confidence: 99%

PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN

Khalaf,

Soliman,

Mohamed

2023

ACS Omega

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Revealing the tertiary structure of proteins holds huge significance as it unveils their vital properties and functions. These intricate three-dimensional configurations comprise diverse interactions including ionic, hydrophobic, and disulfide forces. In certain instances, these structures exhibit missing regions, necessitating the reconstruction of specific segments, thereby resulting in challenges in protein design, which encompasses loop modeling, circular permutation, and interface prediction. To address this problem, we present two pioneering models: pix2pix generative adversarial network (GAN) and PLM-GAN. The pix2pix GAN model is adept at generating and inpainting distance matrices of protein structures, whereas the PLM-GAN model incorporates residual blocks into the U-Net network of the GAN, building upon the foundation of the pix2pix GAN model. To bolster the models' performance, we introduce a novel loss function named the "missing to real regions loss" (L MTR ) within the GAN framework. Additionally, we introduce a distinctive approach of pairing two different distance matrices: one representing the native protein structure and the other representing the same structure with a missing region that undergoes changes in each successive epoch. Moreover, we extend the reconstruction of missing regions, encompassing up to 30 amino acids and increase the protein length by 128 amino acids. The evaluation of our pix2pix GAN and PLM-GAN models on a random selection of natural proteins (4ZCB, 3FJB, and 2REZ) demonstrated promising experimental results. Our models constitute significant contributions to addressing intricate challenges in protein structure design. These contributions hold immense potential to propel advancements in protein−protein interactions, drug design, and further innovations in protein engineering. Data, code, trained models, examples, and measurements are available on https://github.com/mena01/PLM-

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Section: Proposed Methodologymentioning

confidence: 99%

PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN

Khalaf,

Soliman,

Mohamed

2023

ACS Omega

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A Deep Exposition of GAN and its applications

Azahad¹,

Hameeda²

2023

IJETMS

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Generative Adversarial Networks (GANs) have revolutionized the field of machine learning and artificial intelligence by providing a powerful framework for generating realistic and high-quality synthetic data. GANs consist of two networks, a generator that produces synthetic data and a discriminator that distinguishes between the synthetic data and real data. The two networks are trained together in a game-theoretic setting, where the generator tries to produce synthetic data that is similar to the real data, while the discriminator tries to distinguish between the two. This paper provides a deep exposition of GAN and its applications, starting with the basics of GANs, their architecture, and how they work. We then discuss the training process of GAN, the challenges associated with it, and the techniques used to address these issues. We also describe the different variants of GANs, including conditional GAN, progressive GAN, and style-based GAN, and their applications. Next, we provide a comprehensive overview of the various domains where GANs have been successfully applied, such as image and video synthesis, text generation, and music composition. We discuss the potential future directions of GANs and their applications, including research areas that need further investigation. Finally, we highlight the challenges and limitations associated with GANs, such as mode collapse, vanishing gradients, and instability, and the ethical and legal issues associated with their applications. We conclude by summarizing the key points of the paper and highlighting the potential of GANs as a tool for generating realistic and high-quality synthetic data.

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GAN models in natural language processing and image translation

Cited by 2 publications

References 19 publications

PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN

PLM-GAN: A Large-Scale Protein Loop Modeling Using pix2pix GAN

A Deep Exposition of GAN and its applications

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