All About Bioinformatics: From Beginner to Expert

Length: 312 pages
Edition: 1
Language: English
Publisher: Academic Press
Publication Date: 2023-04-25
ISBN-10: 0443152500
ISBN-13: 9780443152504
Sales Rank: #0 (See Top 100 Books)

All About Bioinformatics: From Beginner to Expert provides readers with an overview of the fundamentals and advances in the field of bioinformatics, as well as some future directions. Each chapter is didactically organized and includes introduction, applications, tools, and future directions to cover the topics thoroughly.

The book covers both traditional topics such as biological databases, algorithms, genetic variations, static methods, and structural bioinformatics, as well as contemporary advanced topics such as high-throughput technologies, drug informatics, system and network biology, and machine learning. It is a valuable resource for researchers and graduate students who are interested to learn more about bioinformatics to apply in their research work.

Cover image
Title page
Table of Contents
Copyright
Chapter 1. What is bioinformatics?
    1.1. Introduction
    1.2. History
    1.3. Biological databases
    1.4. Algorithms in computational biology
    1.5. Genetic variation and bioinformatics
    1.6. Structural bioinformatics
    1.7. High-throughput technology
    1.8. Drug informatics
    1.9. System and network biology
    1.10. Machine learning in bioinformatics
    1.11. Bioinformatics workflow management systems
    1.12. Application of bioinformatics
Chapter 2. Introduction to biological databases
    2.1. Introduction
    2.2. Types of databases
    2.3. Models of databases
    2.4. Primary nucleic acid databases
    2.5. Primary protein databases
    2.6. Secondary protein databases
    2.7. Composite sequence databases
    2.8. Genomics and proteomics databases
    2.9. Miscellaneous databases
Chapter 3. Statistical methods in bioinformatics
    3.1. Introduction
    3.2. Statistics at the interface of bioinformatics
    3.3. Measures of central tendency
    3.4. Skewness and kurtosis
    3.5. Variability and its measures
    3.6. Different types of distributions and their significance
    3.7. Sampling
    3.8. Probability
    3.9. Comparing the means of two or more data variables or groups
    3.10. Platforms employed for statistical analysis
    3.11. Gene ontology & pathway analysis
    3.12. Future prospects and conclusion
Chapter 4. Algorithms in computational biology
    4.1. Sequence alignment
    4.2. Pair-wise alignment
    4.3. Dot-matrix method
    4.4. Dynamic programming
    4.5. Scoring matrices
    4.6. Word methods
    4.7. Multiple sequence alignment
    4.8. Phylogenetics
Chapter 5. Genetic variations
    5.1. Introduction
    5.2. Types of variations
    5.3. Effects of genetic variation
    5.4. Biological database
    5.5. Phenotype-genotype association
    5.6. Pharmacogenomics
    5.7. Pharmacogenomics and targeted drug development
    5.8. Computational biology methods for decision support in personalized medicine
Chapter 6. Structural bioinformatics
    6.1. Introduction
    6.2. Viewing protein structures
    6.3. Alignment of protein structures
    6.4. Structural prediction
Chapter 7. High throughput technology
    7.1. Omics theory
    7.2. High-throughput technologies
    7.3. Genomics
    7.4. Epigenomics
    7.5. Transcriptomics
    7.6. Proteomics
    7.7. Metabolomics
Chapter 8. Drug informatics
    8.1. Introduction
    8.2. Computational drug designing and discovery
    8.3. Structure based drug designing
    8.4. Ligand-based drug designing
    8.5. ADMET
    8.6. Drug repurposing
Chapter 9. A machine learning approach to bioinformatics
    9.1. Introduction to machine learning?
    9.2. Types of machine learning systems
    9.3. Evaluation of machine learning models
    9.4. Optimization of models
    9.5. Main challenges of machine learning
Chapter 10. Systems and network biology
    10.1. Introduction
    10.2. Network theory
    10.3. Graph theory
    10.4. Features of biological networks
    10.5. Types of biological networks
    10.6. Sources of data for biological networks
    10.7. Gene ontology for network analysis
    10.8. Analysis of biological networks and interactomes
    10.9. Interaction network construction using a gene list
    10.10. Data analysis tools
    10.11. Network visualization tools
    10.12. Important properties to be inferred from networks
Chapter 11. Bioinformatics workflow management systems
    11.1. Introduction to workflow management systems
    11.2. Galaxy
    11.3. Gene pattern
    11.4. KNIME: The Konstanz information miner
    11.5. LINCS tools
    11.6. Anduril bioinformatics and image analysis
    11.7. NextFlow
Chapter 12. Data handling using Python
    12.1. Introduction
    12.2. Datatypes and operators
    12.3. Variables
    12.4. Strings
    12.5. Python lists and tuples
    12.6. Dictionary in Python
    12.7. Conditional statements
    12.8. Loops in Python
    12.9. File handling in Python
    12.10. Importing functions
    12.11. Data handling
Index