Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review

Abstract

The evolving field of generative artificial intelligence (GenAI), particularly generative deep learning, is revolutionizing a host of scientific and technological sectors. One of the pivotal innovations within this domain is the emergence of generative adversarial networks (GANs). These unique models have shown remarkable capabilities in crafting synthetic data, closely emulating real-world distributions. Notably, their application to gene expression data systems is a fascinating and rapidly growing focus area. Restrictions related to ethical and logistical issues often limit the size, diversity, and data-gathering speed of gene expression data. Herein lies the potential of GANs, as they are capable of producing synthetic gene expression data, offering a potential solution to these limitations. This review provides a thorough analysis of the most recent advancements at this innovative crossroads of GANs and gene expression data, specifically during the period from 2019 to 2023. In the context of the fast-paced progress in deep learning technologies, accurate and inclusive reviews of current practices are critical to guiding subsequent research efforts, sharing knowledge, and catalyzing continual growth in the discipline. This review, through highlighting recent studies and seminal works, serves as a key resource for academics and professionals alike, aiding their journey through the compelling confluence of GANs and gene expression data systems.

A Review of Generative Models for 3D Vehicle Wheel Generation and Synthesis

“…Trained on extensive datasets of existing wheel designs, the models acquire intricate details and styles unique to different wheel types. The generative model then produces entirely new, customized wheel designs while complying with industry standards and safety regulations [26]. Building on the findings from the study [27], for design exploration, integrated topology optimization yielded a new design comparable to the reference, overcoming low compliance through topology optimization applied to an older design (the connection).…”

“…Generative design algorithms optimize structures by iteratively removing material that is not structurally necessary, resulting in lightweight yet robust designs. The approach in [26] aligns to reduce the weight of electric vehicles (EVs) to improve energy efficiency. Reducing the weight of vehicles is crucial to increasing efficiency.…”

Integrating Drone Imagery and AI for Improved Construction Site Management through Building Information Modeling

“…The evaluation then pivoted toward analyzing the quality of generation where GANs surfaced as the most fitting candidate. GAN surpassed other models by displaying superior performance in aspects like generation quality, training speed, and stability [63]. Its prowess in crafting lifelike images deemed it apt for photorealistic rendering, aligning seamlessly with the project's objectives.…”

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

“…Generative adversarial networks (GANs), a game-changer in the field, consist of two neural networks, the generator and the discriminator, that compete [26][27][28][29][30][31][32]. This unique architecture enables the generation of new, synthetic data instances that resemble the training data, a feature that holds promise in drug discovery and personalized medicine.…”