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Artificial Intelligence in Microbiology. Scope and Challenges / Volume-II :

Artificial Intelligence in Microbiology: Scope and Challenges, Volume-II, Volume 56 covers changes due to the emergence of Artificial Intelligence (AI).AI is being used to analyze massive data in a predictable form, about the behavior of microorganisms, to solve microbial classification-related prob...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Srivastava, Akanksha (Editor), Mishra, Vaibhav (Editor)
Format: eBook
Language:English
Published: London, England : Academic Press, [2025]
Edition:First edition.
Series:Methods in microbiology ; Volume 56.
Subjects:
Molecular biology.
Artificial intelligence.
Biologie moléculaire.
Intelligence artificielle.
molecular biology.
artificial intelligence.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Artificial Intelligence in Microbiology: Scope and Challenges Volume 2
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Taking on the resistance: Artificial intelligence (AI) and battle against antimicrobial resistance
  • 1. Introduction
  • 2. Antimicrobial resistance: Mechanisms and prevalence
  • 3. AI application #1: Extracting compounds of value
  • 3.1. Artificial intelligence and unfolding proteins
  • 4. AI application #2: Drug development and creating new antimicrobials
  • 5. AI application #3: Predicting side-effects
  • 6. AI application #4: Developing drugs to reduce biofilms on drug delivery systems
  • 7. AI application #5: Detecting disease likelihood
  • 8. AI application #6: Machine learning to identify the likelihood of gene transfer as an antibiotic resistance mechanism
  • 9. AI application #7: Pathogen biogeography and AI
  • 10. Machine learning approaches to support such inquiries include
  • 11. Summary
  • References
  • Chapter Two: Production of bacteriocins by AI: As food preservative
  • 1. Introduction
  • 1.1. Optimising AI for the fermentation process and training AI-algorithms in screening and identifying bacteriocin-produ ...
  • 1.2. Involvement of multiple steps using AI for bacteriocin production
  • 1.3. Using AI for quality control to ensure standing of using bacteriocin
  • 1.4. Advantages and limitations of using AI for bacteriocins
  • 2. Future of using bacteriocins by AI as food preservative agent
  • 3. Conclusion
  • Conflict of interest
  • References
  • Chapter Three: Artificial intelligence (AI) implementation in the food industry as a promising tool for protecting food f ...
  • 1. Introduction
  • 2. Application of artificial intelligence (AI) in food industry
  • 2.1. Importance of artificial intelligence (AI) in sorting of packages and products.
  • 2.2. Artificial intelligence (AI) in object recognition and classification
  • 2.3. Artificial intelligence (AI) in automated packaging
  • 2.4. Artificial intelligence (AI) in predicting shelf life
  • 2.5. Maintaining cleanliness
  • 2.6. Artificial intelligence (AI) in developing new products
  • 2.7. Assisting customers with decision making
  • 2.8. Artificial intelligence in food processing and distribution
  • 2.9. Supply Chain Optimization
  • 3. Improving Food Safety Using Artificial Intelligence
  • 3.1. Food Spoilage Detection
  • 3.2. Food Preservation Techniques
  • 3.3. Smart Food Life Prediction
  • 3.4. AI Techniques Used for Improving Food Quality and Safety
  • 4. Artificial Intelligence Systems and for Enhancing the Public Health
  • 5. Artificial intelligence´s Role in Alleviating Food Insecurity and Waste Management
  • 5.1. Prospects of the AI in Food Security
  • 5.1.1. Remote sensing
  • 5.1.2. Freshness prediction and categorization
  • 5.1.3. AI and food redistribution systems to at-risk communities
  • 5.2. AI and Food Waste Management
  • 6. Challenges and Limitations of Applying AI in Food Sector
  • 7. The Future Application of Artificial Intelligence in the Food Industry
  • References
  • Chapter Four: Artificial intelligence and microbial cellular intelligence for bioprocess and biofuel
  • 1. Introduction
  • 1.1. Microbial cellulose
  • 1.2. Microbial hemicellulose
  • 1.3. Advanced AI tools and microbial lignin
  • 2. Pretreatment
  • 3. Biofuel types
  • 4. Artificial intelligence (AI) and biofuel production
  • 4.1. Bioethanol
  • 4.2. Biohydrogen
  • 4.3. Biomethane
  • 4.4. Biobutanol
  • 4.5. Bio-diesel
  • 5. Recent advancement in scale up fermentation
  • 6. Role of artificial intelligence in microbial fermentation for biofuel
  • 7. Future prospects of AI in microbial biofuel
  • 8. Conclusions
  • Acknowledgements
  • References.
  • Chapter Five: The power of AI in viral vaccine production: A paradigm shift in efficiency and costs
  • 1. Introduction
  • 2. Vaccine victories: The timeline
  • 3. AI in vaccine development
  • 3.1. Reverse vaccination (RV)
  • 4. Unlocking breakthroughs: AI´s role in revolutionizing COVID-19 vaccine discovery
  • 4.1. Utilizing AI for efficient data collection in COVID-19 vaccine research
  • 4.2. Different approach utilizes for COVID-19 vaccine discovery
  • 4.2.1. COVID-19 vaccine discovery: Protein-based approach
  • 4.2.2. COVID-19 vaccine discovery: RNA-based approach
  • 4.3. Different algorithms used for the vaccine discovery
  • 4.3.1. Network-based algorithms
  • 4.3.2. Expression-based algorithms
  • 4.3.3. Molecular docking algorithms
  • 4.4. AI advances post-marketing vaccine surveillance
  • 4.5. AI revolutionizes vaccine development for Disease X
  • 4.6. Critical initiative for Disease X vigilance
  • 5. Limitations of AI-assisted vaccine development
  • 6. Conclusion and future prospectus
  • References
  • Chapter Six: Role of artificial intelligence in studying metagenomics of microbes: Decoding the microbial sphere
  • 1. Introduction
  • 2. Metagenomics
  • 3. Metagenomic approaches/data types
  • 3.1. Targeted metagenomics via amplicon sequencing/DNA-metabarcoding
  • 3.2. Untargeted met genomics via Shotgun sequencing
  • 3.3. Re-analysed metagenomics
  • 4. Metagenomics data (metdata) and challenges in its handling
  • 5. Artificial intelligence (AI): An efficient way to handle metadata
  • 5.1. Machine learning (ML): Understanding the microbial code
  • 5.1.1. Supervised machine learning (SML)
  • 5.1.2. Deep learning (DL): Uncovering hidden patterns in microbiology
  • 6. AI tools for metadata analysis
  • 7. Challenges to implementing AI and ML in metadata analysis
  • 7.1. Bacterial data heterogeneity analysed by metadata
  • 7.2. Data representation.
  • 7.3. Taxonomy and classification
  • 7.4. Model integration
  • 8. AI in food and environmental microbiological metadata
  • 9. AI in health sciences metadata
  • 10. AI in plant growth promoting bacteria metagenomics
  • 11. Future prospects
  • References
  • Chapter Seven: Revolutionizing bioethanol production: The role of AI in process innovation
  • 1. Introduction
  • 2. What is Artificial Intelligence (AI)?
  • 3. Classification of feedstocks for bioethanol production
  • 3.1. Integration of AI in first-generation feedstock
  • 3.2. Second-generation feedstock and AI
  • 3.3. Involvement of AI in third-generation feedstock
  • 3.4. AI and fourth-generation feedstock
  • 4. Harnessing of divergent microorganisms as a source for bioethanol production
  • 4.1. Fungal strains in bioethanol production
  • 5. Genetically modified microorganisms
  • 6. AI application in bioethanol production stages
  • 7. Enzymes used in hydrolysis
  • 8. AI in fermentation and recovery
  • 9. Conclusions and future perspective
  • References
  • Chapter Eight: AI in infectious disease diagnosis and vaccine development
  • 1. Introduction
  • 1.1. AI in healthcare
  • 2. Infectious-disease diagnostics and treatments
  • 2.1. Enhancing diagnostic accuracy
  • 2.2. Predicting infectious disease outbreaks
  • 2.3. Facilitating personalized treatments
  • 2.4. Ethical and regulatory challenges
  • 3. AI-driven vaccine models for infectious diseases
  • 3.1. VaxELAN
  • 3.1.1. Key tools employed in VaxELAN
  • 3.2. Vaxi-DL
  • 4. nHLAPred tool for MHC class-I prediction
  • 4.1. B-cell epitope prediction tools for infectious diseases
  • 4.2. T-cell epitope prediction tool for infectious disease
  • 4.3. Application of AI techniques in vaccine development for various infectious diseases
  • 5. Advances in AI for infectious-disease surveillance
  • 6. Conclusion
  • 7. Future directions
  • Acknowledgement.