3 Cellular image classification and identification of genetic variations using artificial intelligence
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Jaya
Abstract
Recent developments in cellular imaging and genomic analysis have transformed biomedical research by offering profound insights into cellular structures, molecular interactions, and genetic diversity. This chapter delves into advanced imaging methods, such as fluorescence, confocal, and electron microscopy, which have greatly enhanced our ability to visualize and analyze cellular components. The advent of high-throughput microscopy has further streamlined the process of acquiring images on a large scale, improving the efficiency of biomedical research. At the same time, the utilization of machine learning and deep learning for cellular image classification has resulted in significant enhancements in diagnostic precision, enabling the automated detection of cellular patterns and anomalies. Additionally, the analysis of genetic variations is vital for unraveling disease mechanisms. This chapter highlights important genomic changes, including single nucleotide polymorphisms, insertions, deletions, and structural variations, and discusses their effects on cellular functions and disease development. Cutting-edge genomic technologies, such as whole genome sequencingwhole genome sequencing and next-generation sequencingnext-generation sequencing, have made it possible to accurately identify and categorize these variations. Furthermore, advancements in computational bioinformatics and variant-calling algorithms have improved the reliability of genetic assessments. The merging of cellular imaging with genomic analysis has opened doors for integrated multi-omics strategies, leading to enhancements in disease modeling, biomarker discovery, and precision medicine. By combining imaging data with genomic information, researchers can obtain a more holistic view of cellular processes at various levels, propelling progress in both research and clinical practices. This chapter emphasizes the transformative potential of these interdisciplinary methodologies in improving diagnostic accuracy, therapeutic approaches, and personalized healthcare.
Abstract
Recent developments in cellular imaging and genomic analysis have transformed biomedical research by offering profound insights into cellular structures, molecular interactions, and genetic diversity. This chapter delves into advanced imaging methods, such as fluorescence, confocal, and electron microscopy, which have greatly enhanced our ability to visualize and analyze cellular components. The advent of high-throughput microscopy has further streamlined the process of acquiring images on a large scale, improving the efficiency of biomedical research. At the same time, the utilization of machine learning and deep learning for cellular image classification has resulted in significant enhancements in diagnostic precision, enabling the automated detection of cellular patterns and anomalies. Additionally, the analysis of genetic variations is vital for unraveling disease mechanisms. This chapter highlights important genomic changes, including single nucleotide polymorphisms, insertions, deletions, and structural variations, and discusses their effects on cellular functions and disease development. Cutting-edge genomic technologies, such as whole genome sequencingwhole genome sequencing and next-generation sequencingnext-generation sequencing, have made it possible to accurately identify and categorize these variations. Furthermore, advancements in computational bioinformatics and variant-calling algorithms have improved the reliability of genetic assessments. The merging of cellular imaging with genomic analysis has opened doors for integrated multi-omics strategies, leading to enhancements in disease modeling, biomarker discovery, and precision medicine. By combining imaging data with genomic information, researchers can obtain a more holistic view of cellular processes at various levels, propelling progress in both research and clinical practices. This chapter emphasizes the transformative potential of these interdisciplinary methodologies in improving diagnostic accuracy, therapeutic approaches, and personalized healthcare.
Chapters in this book
- 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
Chapters in this book
- 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
