Statistical modelling and machine learning principles for bioinformatics

✍ Scribed by Srinivasa K.G (ed.)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 318
• Category: Library

✦ Table of Contents

Bioinformatics......Page 6
Protein Structure Prediction and Gene Expression Analysis......Page 7
Genomics and Proteomics......Page 8
Contents......Page 9
About the Editors......Page 11
Bioinformatics......Page 13
1 Introduction......Page 14
3 Importance of€Bioinformatics......Page 15
4 Emergence of€Bioinformatics......Page 16
5 Computational Biology and€Bioinformatics: A€Comparison......Page 17
6 Computational Approaches to€Biological Problems......Page 18
References......Page 20
1.1 Bioinformatics in€Animal Science......Page 21
1.2 Genome Sequencing......Page 22
2 Databases......Page 23
2.2 RNA Specific Resources......Page 24
2.4 Databases on€Mutations and€Variations......Page 25
3.1 BLAST......Page 26
3.2 FASTA......Page 28
3.4 Drug Approval Process in€India......Page 31
References......Page 33
1 Introduction......Page 34
2.1 Artificial Neural Network (ANN) in€Bioinformatics......Page 37
2.2 Decision Trees in€Bioinformatics......Page 38
3 Applying Artificial Neural Network in€Bioinformatics: A€Case Study......Page 39
3.1 Designing ANN for€Bioinformatics......Page 40
3.2 ANN in€Protein Bioinformatics......Page 41
4.1 Data Errors......Page 43
4.3 Approximation Versus Explanation......Page 44
References......Page 45
1 Introduction......Page 49
2.1 The Inception of€Biological Database......Page 51
2.2 Starting Point: Creation of€Sequence Data......Page 53
2.3 Analysis of€Sequence Data......Page 54
3 Case Study BLAST......Page 55
3.1 BLAST Algorithm......Page 56
4.2 Unsupervised Learning......Page 59
4.5 Feature Selection......Page 60
4.7 Machine Learning Approach: Decision Tree......Page 61
4.9 Machine Learning Approach: Clustering......Page 62
5.1 Using Clustering Approach to Identify Patients’ Subtypes......Page 63
5.2 Drug Repositioning Using Classification Approach......Page 64
6 Case Study: Kipoi Utilizing Machine Learning Model for€Genomics......Page 65
7.1 Dirty Biological Database......Page 67
7.3 Approximation and€Explanation......Page 68
8 Conclusions......Page 69
References......Page 70
1 Introduction......Page 71
2 Biomedical Application: Case Study......Page 72
3 Conclusion......Page 80
References......Page 81
1 Introduction......Page 83
1.1 Open-Source Software Tools......Page 85
2.1 Introduction to€Bioperl......Page 86
2.4 Bioperl Installation......Page 88
2.5 Sequence Object......Page 91
3 Biopython......Page 92
3.3 Advantages......Page 93
3.4 Biopython Installation......Page 94
3.5 Sample Case Study......Page 97
3.6 Sample Code for€Sequencing......Page 98
References......Page 99
Protein Structure Prediction and Gene Expression Analysis......Page 100
1 Introduction......Page 101
2.1 Comparative Modelling......Page 102
2.2 Threading......Page 103
2.3 Ab Initio Prediction......Page 104
3 Homology Modelling......Page 105
3.1 Template Recognition and€Initial Alignment......Page 106
4 Loop Modelling......Page 107
5 Use Case......Page 110
6 Conclusion......Page 121
References......Page 123
1 Introduction......Page 125
2 Computational Methods for€Protein Structure Prediction......Page 127
2.1 Homology Modelling Techniques......Page 128
2.2 Protein Threading......Page 129
2.3 Ab Initio Modelling......Page 131
2.4 CASP......Page 134
References......Page 137
1 Introduction......Page 140
2 Gene Regulatory Networks......Page 142
3 Computational Approaches for€Construction of€Gene Regulatory Networks......Page 143
3.1 Ordinary Differential Equations......Page 144
3.2 Neural Networks Method......Page 148
3.3 Boolean Network-Based Methods......Page 149
3.4 Bayesian Network-Based Methods......Page 151
References......Page 153
1 Introduction......Page 155
2 Gene Expression Data......Page 156
3.1 A€Basic Algorithm for€Feature Selection......Page 157
4 Fitness Measures of€a€Feature......Page 160
5 Conclusion......Page 164
References......Page 165
Genomics and Proteomics......Page 166
1 Introduction......Page 167
1.1 Machine Learning in€Bioinformatics [2]......Page 168
2 Unsupervised Techniques in€Bioinformatics [3]......Page 169
2.1 Hierarchical Clustering [4, 5]......Page 170
2.2 Partitional Clustering with€Respect to€Genomics......Page 173
3 Hierarchical Clustering with€Respect to€Genomics [8]......Page 183
3.2 Case Study 1: Charting Evolution Through Phylogenetic Trees......Page 187
3.3 Case Study 2: Use of€Hierarchical Clustering and€Cluster Validation Indices for€Analysis of€Genetic Association [20]......Page 188
References......Page 189
1 Introduction......Page 191
2 Scope of€Data and€Machine Learning in€Proteomics......Page 193
3 Machine Learning in€Proteomics......Page 194
3.2 Sample Datasets Available for€Proteomics......Page 195
3.3 Data Preprocessing Algorithms......Page 196
3.4 Dimension and€Feature Subset Selection......Page 198
3.5 Protein Classification......Page 200
4.1 Decision Trees......Page 201
4.2 Support Vector Machine......Page 202
4.3 Random Forest......Page 203
4.5 K-Nearest Neighbor......Page 204
4.6 Classification Trees......Page 205
5.1 Proteomic Mass Spectra Classification Using Decision Tree Technique [17]......Page 207
5.2 Distance Metric Learning and€Support Vector Machine for€Classification of€Mass Spectrometry Proteomics Data [19]......Page 210
6 Conclusion......Page 212
References......Page 213
1 Introduction......Page 214
2 Genetic Code......Page 215
2.1 Canonical Genetic Code and€Mechanism of€Its Realization......Page 216
2.2 Visual Representations of€the€Genetic Code: Tables, Wheels, Hypercubes......Page 218
2.3 Modeling Genetic Codes: What Is Learned from€Massive Codon Reassignments in€Silico......Page 222
3 Estimating and€Visualizing k-mer Occurrence in€Genomic Sequences......Page 225
3.2 Unmet Opportunities......Page 226
4 Codon Indices......Page 227
4.1 Definition and€Diversity of€Codon Bias Indices......Page 228
4.2 Biological Significance of€Several Popular Codon Indices......Page 229
4.3 Online Applications and€Databases to€Calculate and€Visualize Codon Indices......Page 240
4.4 Future Directions......Page 242
5.1 Cases and€Reasons for€Nonrandom Codon Co-occurrence......Page 243
5.2 Anaconda—Software to Visualize Codon Pairs......Page 247
5.3 Codon Utilization Tool (CUT)—A Database of k-Codon Usage for Yeast, Rat and Mice......Page 249
6.1 CUB Types and€Plausible Biological Reasons......Page 250
6.2 Visualizing CUB Between and€Within Genes......Page 252
7.1 Basic Concept......Page 254
7.2 Diversity of€CSMs......Page 259
7.3 Simulation of€Codon Substitution Patterns......Page 262
7.4 Approaches Toward Visual Representation of€CSMs......Page 263
8 Identification and€Visualization of€Selection Forces Acting on€Coding Sequences......Page 266
8.1 Molecular Evolution at€the€Codon Level: A€Brief Introduction......Page 267
8.2 Tools for€Visual Representation of€Selective Pressure on€Protein-Coding Sequences......Page 270
9 Outlook......Page 271
References......Page 273
1 Introduction to€Tumor Ecosystem and€Single-Cell Analysis......Page 290
1.1 Major Components of€Tumor Microenvironment and€Their Role in€Carcinogenesis......Page 292
1.2 Conventional Sequencing Approaches and€Their Limitation to€Characterize TME......Page 294
2 Single-Cell omics......Page 295
2.1 Single-Cell Genomics......Page 297
2.2 Single-Cell Epigenomics......Page 298
2.3 Single-Cell Transcriptomics......Page 300
3 Cancer: Dissecting Tumor Heterogeneity......Page 301
3.1 Multiregion Sequencing and€Tumor Heterogeneity......Page 302
3.2 Clonal Expansion Models......Page 303
4 Single-Cell Analysis: Drug Resistance Mechanisms......Page 305
4.1 Mechanisms of€Drug Resistance......Page 306
5 Circulating Tumor Cells: Significance of€Single-Cell Analysis......Page 309
6 Limitations and€Future Directions......Page 311
References......Page 312