Translational Bioinformatics /

Translational Bioinformatics is an emerging field in the direction of biomedical research. High throughput technologies can be applied to the generated biological data to develop the vaccine and personalized medicine. This volume consists of the chapters from different stalwart of the field covering...

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Bibliographic Details
Main Authors:	Donev, Rossen (Author), Prajapati, Vijay Kumar (Author)
Corporate Author:	ScienceDirect (Online service)
Format:	eBook
Language:	English
Published:	San Diego, CA : Zoe Kruze, [2024]
Edition:	First edition.
Series:	Advances in protein chemistry and structural biology ; Volume 139.
Subjects:	Bioinformatics. Bio-informatique. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Title Page
Copyright
Contents
Contributors
Preface
Chapter One: From genome to clinic: The power of translational bioinformatics in improving human healthTranslational Bioinformatics: Genomic Advances in Human Health
1 Introduction
2 Data sources and methods used in translational bioinformatics
3 Impact of translational bioinformatics on human health
4 Methods in network biology for the study of complex diseases
4.1 Physical interaction networks
4.2 Functional interaction networks
5 Challenges and limitations of translational bioinformatics
6 Future of translational bioinformatics and its potential impact on medicine and human health
7 Conclusion
References
Chapter Two: Computational resources and chemoinformatics for translational health research
1 Introduction
2 Overview of chemoinformatics and its applications
2.1 Drug discovery and development
2.2 Chemical property prediction
2.3 QSAR/QSPR modelling
2.4 Virtual screening and lead optimization
2.5 Personalized medicine
3 Computational tools and databases for translational health
3.1 Biological databases
3.1.1 Genomic databases
3.1.2 Proteomic databases
3.1.3 Metabolomic databases
3.2 Clinical databases
3.3 Computational tools for data integration and analysis
3.3.1 Data mining and machine learning algorithms
3.3.2 Network analysis tools
3.3.3 Data visualization tools
4 Chemoinformatics approaches in translational health
4.1 Ligand-based drug design strategies
4.1.1 Pharmacophore modelling
4.1.2 Quantitative structure-activity relationship (QSAR) modelling
4.2 Structure-based drug design strategies
4.2.1 Protein structure prediction and modelling
4.2.2 Molecular docking and virtual screening
4.3 Molecular dynamics simulations.
4.4 Chemogenomics and target identification
5 Translational applications of computational resources and chemoinformatics
5.1 Drug discovery and repurposing
5.2 Personalized medicine and biomarker discovery
5.3 Predictive modelling and risk assessment
5.4 Pharmacokinetics and pharmacodynamics prediction
5.5 Chemoinformatics in clinical trials
6 Challenges and future perspectives
6.1 Data quality and standardization
6.2 Ethical considerations
6.3 Integration of multi-omics data
6.4 Artificial intelligence and machine learning
6.5 Collaboration
7 Conclusions
References
Chapter Three: Technological advancements in viral vector designing and optimization for therapeutic applications
1 Introduction
2 In silico approaches for viral vector designing
2.1 Methodologies for in silico viral vector designing
3 Data integration and mining for vector optimization
4 High-throughput screening and analysis
5 Regulatory considerations and safety assessment
6 Translation of viral vector research in clinical practices and future perspectives
7 Conclusion
References
Chapter Four: A journey from omics to clinicomics in solid cancers: Success stories and challenges
1 Introduction
2 Multi-omics studies in cancers
2.1 Genomics
2.2 Transcriptomics
2.2.1 Transcriptomics based studies in breast cancer (BC)
2.3 Epigenomics and methylomics
2.3.1 Methylation specific diagnostic biomarkers in BC
2.3.2 Methylation specific biomarkers for treatment response/recurrence in BC
2.4 Proteomics, peptidomics and interactomics
2.4.1 Proteomics studies in BC
2.5 Metabolomics and lipidomics
2.5.1 Lipidomics
2.6 Radiomics
3 Challenges in integration of multi-omics and clinical data in cancer
4 Use of AI and ML in integrating multi-omics and clinical data in cancer research.
4.1 AI-based tools in cancer diagnosis
4.2 AI based tools for cancer classification, and grading
4.3 AI based tools for radiomics
4.4 AI for prediction of cancer prognosis and treatment response
4.5 AI based tools for cancer drug discovery and repurposing
4.6 Some other AI based onco-omics tools
5 Challenges in AI guided clinicomics in cancer research
5.1 Data quality and quantity
5.2 Data heterogeneity
5.3 Interpretability and explainability
5.4 Bias and generalization
5.5 Ethical, legal and social implications
6 Conclusion
References
Chapter Five: Computational approaches for identifying disease-causing mutations in proteins
1 Introduction
2 Databases for disease-causing and neutral mutations
2.1 Humsavar
2.2 Clinvar
2.3 1000 genomes
2.4 HuVarBase
2.5 MutHTP
2.6 dbCPM
2.7 DoCM
2.8 Online Mendelian Inheritance in Man (OMIM) database
2.9 DisGeNet
2.10 The Human Gene Mutation Database (HDMD)
3 Features for identifying disease-causing mutations
3.1 Sequence-based properties
3.1.1 Physicochemical properties
3.1.2 Predicted secondary structure and solvent accessibility
3.1.3 Motifs
3.1.4 Amino acid composition
3.1.5 Domain location
3.1.6 Position-specific scoring matrices (PSSM) profiles
3.1.7 Mutation matrices
3.1.8 Conservation scores
3.1.9 Neighboring residue information based on amino acid groups
3.2 Structure-based features
3.3 Network-based features
4 Characteristic features of disease-causing mutations
4.1 Frequency of amino acid mutations in disease-prone and neutral sites
4.2 Preferred motifs for disease-causing and neutral mutations
5 Identification of disease-causing hotspot residues
5.1 Hotspot residues in lung cancer
5.2 Molecular signaling of mutational hotspots
5.3 Prediction of hotspot residues.
6 Identification of disease-causing mutations
6.1 Driver mutations in epidermal growth factor receptor
6.2 Disease-causing mutations in glioblastoma
6.3 Discrimination of disease-causing and driver mutations in membrane proteins
6.4 Identification of Alzheimer's disease-causing mutations
6.5 Prediction of frequently mutating sites in covid-19
7 Conclusions
Acknowledgments
References
Further reading
Chapter Six: Uncovering the secrets of resistance: An introduction to computational methods in infectious disease research
1 Introduction
2 Mechanisms of resistance mutations and development of AMR
3 Understanding the implications of resistance mutations and preparative countermeasures
4 Experimental methods available to evaluate resistance mutations
4.1 Phenotypic methods
4.2 Genotypic methods
4.3 Phenotypic-genotypic correlation methods
4.4 Functional assays
4.5 High-throughput screening methods
5 Limitations of the experimental methods to evaluate resistance mutations
5.1 Phenotypic methods
5.2 Genotypic methods
5.3 Phenotypic-genotypic correlation methods
5.4 Functional assays
5.5 High-throughput screening methods
6 Major computational methods to predict resistance mutations and tackle AMR
6.1 Genomic data analysis
6.2 Structural data analysis
6.3 Machine learning and predictive modeling
6.4 Network analysis and systems biology
6.5 Data integration and visualization
7 Advantages and limitations of the computational methods to tackle AMR
7.1 Genomic data analysis
7.2 Predictive modeling and machine learning
7.3 Network analysis and systems biology
7.4 Data integration and visualization
8 ResScan-design protocol and its applications in identifying resistance mutations
9 Conclusion
Acknowledgments
References.
Chapter Seven: Translational bioinformatics approach to combat cardiovascular disease and cancers
1 Introduction
2 Omics-based divisions of translational bioinformatics
2.1 Genomics
2.2 Pharmacogenomics and pharmacovigilance
2.3 Proteomics in translational bioinformatics
2.4 Clinical big data and epidemiology
2.5 Artificial intelligence and personalized medicine
3 Data-security as an issue in translational bioinformatics
4 Translational bioinformatics to combat cardiovascular disorders
4.1 Application of systems biology in cardiovascular research
4.1.1 Application of genomics in CVDs
4.1.2 Application of epigenomics and transcriptomics in CVDs
4.1.3 Application of proteomics in CVDs
4.1.4 Application of metabolomics and lipidomics in CVDs
4.1.5 Application of microbiomics in CVDs
4.1.6 Application of phenomics in CVDs
4.1.7 Multi-omics approaches to CVD
5 Translational bioinformatics to combat cancer
5.1 Application of translational bioinformatics in detecting mutations to treat cancers
5.2 Translational bioinformatics in epitope prediction in cancer samples
5.3 Application of translational bioinformatics in TCR sequencing
5.4 Translational bioinformatics in immune cell quantification
6 Future of translational bioinformatics to design treatment strategies beyond CVDs and cancers
7 Conclusion
Acknowledgments
References
Chapter Eight: Nanoinformatics based insights into the interaction of blood plasma proteins with carbon based nanomaterials: Implications for biomedical applications
1 Introduction
1.1 Carbon-based nanomaterials
1.2 Blood plasma proteins
1.3 Understanding the bio-nano interface
1.4 Present challenges
2 Study of bio-nano interface via computational simulation
2.1 Molecular dynamics simulation.