Computation in bioinformatics : multidisciplinary applications / / editors, S. Balamurugan [et al.]

• Pubblicazione: Hoboken, NJ : , : John Wiley & Sons, Inc., , 2021
• Descrizione fisica: 1 online resource (352 pages)
• Disciplina: 570.285
• Soggetto topico: Bioinformatics; Computational biology
• Persona (resp. second.): Pālamurukan̲Ca
• Nota di contenuto: Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Bioinfomatics as a Tool in Drug Designing -- 1.1 Introduction -- 1.2 Steps Involved in Drug Designing -- 1.2.1 Identification of the Target Protein/Enzyme -- 1.2.2 Detection of Molecular Site (Active Site) in the Target Protein -- 1.2.3 Molecular Modeling -- 1.2.4 Virtual Screening -- 1.2.5 Molecular Docking -- 1.2.6 QSAR (Quantitative Structure-Activity Relationship) -- 1.2.7 Pharmacophore Modeling -- 1.2.8 Solubility of Molecule -- 1.2.9 Molecular Dynamic Simulation -- 1.2.10 ADME Prediction -- 1.3 Various Softwares Used in the Steps of Drug Designing -- 1.4 Applications -- 1.5 Conclusion -- References -- 2 New Strategies in Drug Discovery -- 2.1 Introduction -- 2.2 Road Toward Advancement -- 2.3 Methodology -- 2.3.1 Target Identification -- 2.3.2 Docking-Based Virtual Screening -- 2.3.3 Conformation Sampling -- 2.3.4 Scoring Function -- 2.3.5 Molecular Similarity Methods -- 2.3.6 Virtual Library Construction -- 2.3.7 Sequence-Based Drug Design -- 2.4 Role of OMICS Technology -- 2.5 High-Throughput Screening and Its Tools -- 2.6 Chemoinformatic -- 2.6.1 Exploratory Data Analysis -- 2.6.2 Example Discovery -- 2.6.3 Pattern Explanation -- 2.6.4 New Technologies -- 2.7 Concluding Remarks and Future Prospects -- References -- 3 Role of Bioinformatics in Early Drug Discovery: An Overview and Perspective -- 3.1 Introduction -- 3.2 Bioinformatics and Drug Discovery -- 3.2.1 Structure-Based Drug Design (SBDD) -- 3.2.2 Ligand-Based Drug Design (LBDD) -- 3.3 Bioinformatics Tools in Early Drug Discovery -- 3.3.1 Possible Biological Activity Prediction Tools -- 3.3.2 Possible Physicochemical and Drug-Likeness Properties Verification Tools -- 3.3.3 Possible Toxicity and ADME/T Profile Prediction Tools -- 3.4 Future Directions With Bioinformatics Tool. ; 3.5 Conclusion -- Acknowledgements -- References -- 4 Role of Data Mining in Bioinformatics -- 4.1 Introduction -- 4.2 Data Mining Methods/Techniques -- 4.2.1 Classification -- 4.2.1.1 Statistical Techniques -- 4.2.1.2 Clustering Technique -- 4.2.1.3 Visualization -- 4.2.1.4 Induction Decision Tree Technique -- 4.2.1.5 Neural Network -- 4.2.1.6 Association Rule Technique -- 4.2.1.7 Classification -- 4.3 DNA Data Analysis -- 4.4 RNA Data Analysis -- 4.5 Protein Data Analysis -- 4.6 Biomedical Data Analysis -- 4.7 Conclusion and Future Prospects -- References -- 5 In Silico Protein Design and Virtual Screening -- 5.1 Introduction -- 5.2 Virtual Screening Process -- 5.2.1 Before Virtual Screening -- 5.2.2 General Process of Virtual Screening -- 5.2.2.1 Step 1 (The Establishment of the Receptor Model) -- 5.2.2.2 Step 2 (The Generation of Small-Molecule Libraries) -- 5.2.2.3 Step 3 (Molecular Docking) -- 5.2.2.4 Step 4 (Selection of Lead Protein Compounds) -- 5.3 Machine Learning and Scoring Functions -- 5.4 Conclusion and Future Prospects -- References -- 6 New Bioinformatics Platform-Based Approach for Drug Design -- 6.1 Introduction -- 6.2 Platform-Based Approach and Regulatory Perspective -- 6.3 Bioinformatics Tools and Computer-Aided Drug Design -- 6.4 Target Identification -- 6.5 Target Validation -- 6.6 Lead Identification and Optimization -- 6.7 High-Throughput Methods (HTM) -- 6.8 Conclusion and Future Prospects -- References -- 7 Bioinformatics and Its Application Areas -- 7.1 Introduction -- 7.2 Review of Bioinformatics -- 7.3 Bioinformatics Applications in Different Areas -- 7.3.1 Microbial Genome Application -- 7.3.2 Molecular Medicine -- 7.3.3 Agriculture -- 7.4 Conclusion -- References -- 8 DNA Microarray Analysis: From Affymetrix CEL Files to Comparative Gene Expression -- 8.1 Introduction -- 8.2 Data Processing. ; 8.2.1 Installation of Workflow -- 8.2.2 Importing the Raw Data for Processing -- 8.2.3 Retrieving Sample Annotation of the Data -- 8.2.4 Quality Control -- 8.3 Normalization of Microarray Data Using the RMA Method -- 8.3.1 Background Correction -- 8.3.2 Normalization -- 8.3.3 Summarization -- 8.4 Statistical Analysis for Differential Gene Expression -- 8.5 Conclusion -- References -- 9 Machine Learning in Bioinformatics -- 9.1 Introduction and Background -- 9.1.1 Bioinformatics -- 9.1.2 Text Mining -- 9.1.3 IoT Devices -- 9.2 Machine Learning Applications in Bioinformatics -- 9.3 Machine Learning Approaches -- 9.4 Conclusion and Closing Remarks -- References -- 10 DNA-RNA Barcoding and Gene Sequencing -- 10.1 Introduction -- 10.2 RNA -- 10.3 DNA Barcoding -- 10.3.1 Introduction -- 10.3.2 DNA Barcoding and Molecular Phylogeny -- 10.3.3 Ribosomal DNA (rDNA) of the Nuclear Genome (nuDNA)-ITS -- 10.3.4 Chloroplast DNA -- 10.3.5 Mitochondrial DNA -- 10.3.6 Molecular Phylogenetic Analysis -- 10.3.7 Metabarcoding -- 10.3.8 Materials for DNA Barcoding -- 10.4 Main Reasons of DNA Barcoding -- 10.5 Limitations/Restrictions of DNA Barcoding -- 10.6 RNA Barcoding -- 10.6.1 Overview of the Method -- 10.7 Methodology -- 10.7.1 Materials Required -- 10.7.2 Barcoded RNA Sequencing High-Level Mapping of Single-Neuron Projections -- 10.7.3 Using RNA to Trace Neurons -- 10.7.4 A Life Conservation Barcoder -- 10.7.5 Gene Sequencing -- 10.7.5.1 DNA Sequencing Methods -- 10.7.5.2 First-Generation Sequencing Techniques -- 10.7.5.3 Maxam's and Gilbert's Chemical Method -- 10.7.5.4 Sanger Sequencing -- 10.7.5.5 Automation in DNA Sequencing -- 10.7.5.6 Use of Fluorescent-Marked Primers and ddNTPs -- 10.7.5.7 Dye Terminator Sequencing -- 10.7.5.8 Using Capillary Electrophoresis -- 10.7.6 Developments and High-Throughput Methods in DNA Sequencing -- 10.7.7 Pyrosequencing Method. ; 10.7.8 The Genome Sequencer 454 FLX System -- 10.7.9 Illumina/Solexa Genome Analyzer -- 10.7.10 Transition Sequencing Techniques -- 10.7.11 Ion-Torrent's Semiconductor Sequencing -- 10.7.12 Helico's Genetic Analysis Platform -- 10.7.13 Third-Generation Sequencing Techniques -- 10.8 Conclusion -- Abbreviations -- Acknowledgement -- References -- 11 Bioinformatics in Cancer Detection -- 11.1 Introduction -- 11.2 The Era of Bioinformatics in Cancer -- 11.3 Aid in Cancer Research via NCI -- 11.4 Application of Big Data in Developing Precision Medicine -- 11.5 Historical Perspective and Development -- 11.6 Bioinformatics-Based Approaches in the Study of Cancer -- 11.6.1 SLAMS -- 11.6.2 Module Maps -- 11.6.3 COPA -- 11.7 Conclusion and Future Challenges -- References -- 12 Genomic Association of Polycystic Ovarian Syndrome: Single-Nucleotide Polymorphisms and Their Role in Disease Progression -- 12.1 Introduction -- 12.2 FSHR Gene -- 12.3 IL-10 Gene -- 12.4 IRS-1 Gene -- 12.5 PCR Primers Used -- 12.6 Statistical Analysis -- 12.7 Conclusion -- References -- 13 An Insight of Protein Structure Predictions Using Homology Modeling -- 13.1 Introduction -- 13.2 Homology Modeling Approach -- 13.2.1 Strategies for Homology Modeling -- 13.2.2 Procedure -- 13.3 Steps Involved in Homology Modeling -- 13.3.1 Template Identification -- 13.3.2 Sequence Alignment -- 13.3.3 Backbone Generation -- 13.3.4 Loop Modeling -- 13.3.5 Side Chain Modeling -- 13.3.6 Model Optimization -- 13.3.6.1 Model Validation -- 13.4 Tools Used for Homology Modeling -- 13.4.1 Robetta -- 13.4.2 M4T (Multiple Templates) -- 13.4.3 I-Tasser (Iterative Implementation of the Threading Assembly Refinement) -- 13.4.4 ModBase -- 13.4.5 Swiss Model -- 13.4.6 PHYRE2 (Protein Homology/Analogy Recognition Engine 2) -- 13.4.7 Modeller -- 13.4.8 Conclusion -- Acknowledgement -- References. ; 14 Basic Concepts in Proteomics and Applications -- 14.1 Introduction -- 14.2 Challenges on Proteomics -- 14.3 Proteomics Based on Gel -- 14.4 Non-Gel-Based Electrophoresis Method -- 14.5 Chromatography -- 14.6 Proteomics Based on Peptides -- 14.7 Stable Isotopic Labeling -- 14.8 Data Mining and Informatics -- 14.9 Applications of Proteomics -- 14.10 Future Scope -- 14.11 Conclusion -- References -- 15 Prospects of Covalent Approaches in Drug Discovery: An Overview -- 15.1 Introduction -- 15.2 Covalent Inhibitors Against the Biological Target -- 15.3 Application of Physical Chemistry Concepts in Drug Designing -- 15.4 Docking Methodologies-An Overview -- 15.5 Importance of Covalent Targets -- 15.6 Recent Framework on the Existing Docking Protocols -- 15.7 SN2 Reactions in the Computational Approaches -- 15.8 Other Crucial Factors to Consider in the Covalent Docking -- 15.8.1 Role of Ionizable Residues -- 15.8.2 Charge Regulation -- 15.8.3 Charge-Charge Interactions -- 15.9 QM/MM Approaches -- 15.10 Conclusion and Remarks -- Acknowledgements -- References -- Index -- Also of Interest -- Check out these published and forthcoming related titles from Scrivener Publishing -- EULA.
• Titolo autorizzato: Computation in bioinformatics
• ISBN: 1-119-65476-9; 1-119-65480-7; 1-119-65475-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910555012203321
• Lo trovi qui: Univ. Federico II
• Serie: Artificial intelligence and soft computing for industrial transformation.