Description

Book Synopsis

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.


Artificial Intelligence AI in Cell and Genetic

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    A Hardback by Sudip Mandal

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      Publisher: Humana
      Publication Date: Publication Date: 8/11/2025
      ISBN13: 9781071646892, 978-1071646892
      ISBN10: 1071646893
      Also in:
      Biomedical engineering / Medical engineering

      Description

      Book Synopsis

      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.


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