15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare
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Hem Chandra Pant
Abstract
Clinical microbiology’sclinical microbiology’s use of artificial intelligence (AI) has the potential to enhance pathogen identification, illness comprehension, therapy development, and efficacy, rate, and precision. This is a perfect shift required in clinical microbiology, gene identification, and diagnostic methods. This chapter examines how clinical microbiology and a more profound comprehension of illness can be transformed AI, with a focus on how advanced computational techniques are changing diagnostic approaches to improve human health. The chapter focuses on AIartificial intelligence (AI) in clinical microbiology, drug screeningdrug screening and advancement, with a particular emphasis on reinforcement learning (RL)reinforcement learning (RL) and generative adversarial networks (GANs).generative adversarial networks (GANs). Compared to conventional drug development, AI makes it possible to generate and optimize chemical compounds in an efficient and economical manner. While RL may be used to improve and forecast the biological activity and toxicity profiles of these chemical structures, GANs can be used to develop new molecular structures. By integrating the benefits of 338each approach, this combination provides a tested method for drug discovery that effectively generates and optimizes possible therapeutictherapeutic candidates.
To completely realize the benefits of AI in clinical microbiology, the chapter ends by outlining prospective advances and future prospects in AI-driven diagnostics.
Abstract
Clinical microbiology’sclinical microbiology’s use of artificial intelligence (AI) has the potential to enhance pathogen identification, illness comprehension, therapy development, and efficacy, rate, and precision. This is a perfect shift required in clinical microbiology, gene identification, and diagnostic methods. This chapter examines how clinical microbiology and a more profound comprehension of illness can be transformed AI, with a focus on how advanced computational techniques are changing diagnostic approaches to improve human health. The chapter focuses on AIartificial intelligence (AI) in clinical microbiology, drug screeningdrug screening and advancement, with a particular emphasis on reinforcement learning (RL)reinforcement learning (RL) and generative adversarial networks (GANs).generative adversarial networks (GANs). Compared to conventional drug development, AI makes it possible to generate and optimize chemical compounds in an efficient and economical manner. While RL may be used to improve and forecast the biological activity and toxicity profiles of these chemical structures, GANs can be used to develop new molecular structures. By integrating the benefits of 338each approach, this combination provides a tested method for drug discovery that effectively generates and optimizes possible therapeutictherapeutic candidates.
To completely realize the benefits of AI in clinical microbiology, the chapter ends by outlining prospective advances and future prospects in AI-driven diagnostics.
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Kapitel in diesem Buch
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
