Deep learning, an artificial neural network approach, has transformed several industries, such as statistical proof and autonomous transport systems. However, it faces challenges in biomedical sciences, particularly vaccine research. Despite these, deep learning has great potential in eradicating global infectious diseases. The COVID-19 pandemic highlighted the importance of understanding and anticipating virus hosts. A study using recurrent neural network structure and convolutional (ViRNN) outperformed techniques like the k-nearest neighbor algorithm and deep learning-based algorithms. Deep learning (DL) and artificial intelligence (AI)-driven technologies are revolutionizing drug development by predicting interactions between proteins and sugars. These technologies capture high-level information to create models for quick drug development and enhance novel therapies and preventative measures in viral pathogenesis. DL methods capture genetic and molecular data sources, while AI-driven technologies forecast interactions between proteins and sugars. This work examines data production methodologies and datasets for various illnesses, focusing on simple DL algorithms, accessible tools, and visualization techniques. AI enhances vaccine creation by improving accuracy and efficiency through epitope prediction and antigen selection. It uses data from immune system interactions, protein structures, and genetics to prioritize antigens. However, regulatory concerns and heterogeneity remain challenges, necessitating the integration of advanced technologies. The article also reviews virtual screening-based drug repurposing research, machine learning, DL, and viral disease databases and technologies. It covers case studies unique to each malignancy, the processes underlying viral malignancies, and the drawbacks and difficulties of different strategies. Drug repurposing aims to reduce clinical trial failures by identifying alternate applications for FDA-approved medications used to treat viral cancer. DL is a crucial tool in clinical virology, enhancing diagnostic precision, therapeutic treatments, and epidemiological monitoring. It accelerates medication discovery, predicts viral outbreaks, and enhances viral sequencing, but challenges like algorithmic bias, data security, and ethical concerns persist. The concerned chapter throws light on the in-depth discussion of the application of DL in the discovery of intricate information about virology, highlighting many different research works taking place in this field. The fast-paced progress of the concept of machine learning, along with the discoveries of much-undiscovered information and in-depth analysis, has become the prime subject topic of the chapter, which definitely comes with the immense help of professionals, students, academicians, and researchers as well.

Applications of Deep Learning in Virology

章节
发表日期：

2025年

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Arpita Shome,
Gunjan Mukherjee,
Ankita Shome

摘要

Deep learning, an artificial neural network approach, has transformed several industries, such as statistical proof and autonomous transport systems. However, it faces challenges in biomedical sciences, particularly vaccine research. Despite these, deep learning has great potential in eradicating global infectious diseases. The COVID-19 pandemic highlighted the importance of understanding and anticipating virus hosts. A study using recurrent neural network structure and convolutional (ViRNN) outperformed techniques like the k-nearest neighbor algorithm and deep learning-based algorithms. Deep learning (DL) and artificial intelligence (AI)-driven technologies are revolutionizing drug development by predicting interactions between proteins and sugars. These technologies capture high-level information to create models for quick drug development and enhance novel therapies and preventative measures in viral pathogenesis. DL methods capture genetic and molecular data sources, while AI-driven technologies forecast interactions between proteins and sugars. This work examines data production methodologies and datasets for various illnesses, focusing on simple DL algorithms, accessible tools, and visualization techniques. AI enhances vaccine creation by improving accuracy and efficiency through epitope prediction and antigen selection. It uses data from immune system interactions, protein structures, and genetics to prioritize antigens. However, regulatory concerns and heterogeneity remain challenges, necessitating the integration of advanced technologies. The article also reviews virtual screening-based drug repurposing research, machine learning, DL, and viral disease databases and technologies. It covers case studies unique to each malignancy, the processes underlying viral malignancies, and the drawbacks and difficulties of different strategies. Drug repurposing aims to reduce clinical trial failures by identifying alternate applications for FDA-approved medications used to treat viral cancer. DL is a crucial tool in clinical virology, enhancing diagnostic precision, therapeutic treatments, and epidemiological monitoring. It accelerates medication discovery, predicts viral outbreaks, and enhances viral sequencing, but challenges like algorithmic bias, data security, and ethical concerns persist. The concerned chapter throws light on the in-depth discussion of the application of DL in the discovery of intricate information about virology, highlighting many different research works taking place in this field. The fast-paced progress of the concept of machine learning, along with the discoveries of much-undiscovered information and in-depth analysis, has become the prime subject topic of the chapter, which definitely comes with the immense help of professionals, students, academicians, and researchers as well.