All about bioinformatics : from beginner to expert / by Yasha Hasija. -- London : Academic Press, 2023. – (58.17115/H348)

Contents

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
References
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
References
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
References
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
References
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
References
CHAPTER 6  Structural bioinformatics
6.1 Introduction
6.2 Viewing protein structures
6.3 Alignment of protein structures
6.4 Structural prediction
References
CHAPTER 7  High throughput technology
7.1 0mics theory
7.2 High-throughput technologies
7.3 Genomics
7.4 Epigenomics
7.5 Transcriptomics
7.6 Proteomics
7.7 Metabolomics
References
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
References
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
References
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
References
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
References
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
References
Index