Artificial Intelligence (AI) in Cell and Genetic Engineering

This volume focuses on how different artificial intelligence (AI) techniques like Artificial Neural Network, Support Vector Machine, Random Forest, k-means Clustering, Rough Set Theory, and Convolutional Neural Network models are used in areas of cell and genetic engineering. The chapters this book cover a variety of topics such as molecular modelling in drug discovery, design of precision medicine, protein structure prediction, and analysis using AI. **Readers can also learn about AI-based biomolecular spectroscopy, cell culture-system, AI-based drug discovery, and next generation sequencing. The book also discusses the application of AI in analysis of genetic diseases such as finding genetic insights of oral and maxillofacial cancer, early screening and diagnosis of autism, and classification of breast cancer microarray data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, Artificial Intelligence (AI) in Cell and Genetic Engineering is a valuable resource for readers in various research communities who want to learn more about the real-life application of artificial intelligence and machine learning in systems biology, biotechnology, bioinformatics, and health-informatics especially in the field of cell and genetic engineering.

Includes cutting-edge methods and protocols Provides step-by-step detail essential for reproducible results Contains key notes and implementation advice from the experts

Inhalt
Overview of Molecular Modelling in Drug Discovery with a Special Emphasis on the Applications of Artificial Intelligence.- Integrative AI-Based Approaches to Connect the Multiome to Use Microbiome-Metabolome Interactive Outcome as Precision Medicine.- Artificial Intelligence (AI) Based Protein Structure Prediction and Analysis.- Artificial Intelligence in Cellular and Biomolecular Spectroscopy: A New Horizon.- R-Based Protocols to Predict Synthetic Lethal Interactions in Cancers using Machine-Learning Tools.- Advancements in AI for Computational Biology and Bioinformatics: A Comprehensive Review.- Integrating Genetic Insights and Artificial Intelligence for Enhanced Oral and Maxillofacial Cancer Care.- AI-Based Drug Discovery and Design for Different Genetic Designs.- AI-Assisted Cell Culture-System.- Review on Advancement of AI in Cell Engineering and Molecular Biology.- High-Throughput Virtual Screening of Small Molecule Modulators against Viral Proteins.- AI Revolutionizing Cell and Genetic Engineering: Innovations and Applications.- Recent Developments in the Application of Artificial Intelligence and Machine Learning in Early Screening and Diagnosis of Autism.- Artificial Intelligence in CRISPR-Cas Systems: A Review of Tool Applications.- Machine Learning Approaches for the Identification of Genetic Interactions.- Artificial Intelligence-Based Genome Editing in CRISPR/Cas9.- Harnessing the Power of AI in Cell and Genetic Engineering.- MLCDL: A Critical Practice and Implementation of Multi-Tissue Classification and Diagnosis Using Deep Learning Algorithm.- Classification of Breast Cancer Microarray Data and Identification of Responsible Genes using Rough Set Theory.- Deep-Genomics: Deep Learning Based Analysis of Genome-Sequenced Data for Identification of Gene Alterations.- The Use of AI for Phenotype-Genotype Mapping.- Interface of Artificial Intelligence with Conventional Biostatistics in Healthcare Research.- Review on Advancement of AI in Nutrigenomics.- In Silico Validation of AI-Assisted Drugs in Healthcare.- From DNA to Big Data: NGS Technologies and Their Applications.- Review on Advancement of AI in Synthetic Biology.