Data Analytics in Bioinformatics: A Machine Learning Perspective

521 Pages·2021·16.644 MB·English

by  Rabinarayan Satpathy (editor), Tanupriya Choudhury (editor), Suneeta Satpathy (editor), Sachi Nandan Mohanty (editor), Xiaobo Zhang (editor)

Data Analytics in
Bioinformatics
Scrivener Publishing
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Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Data Analytics in
Bioinformatics
A Machine Learning Perspective
Edited by
Rabinarayan Satpathy
Tanupriya Choudhury
Suneeta Satpathy
Sachi Nandan Mohanty
and
Xiaobo Zhang
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA
and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-78553-8
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xix
Acknowledgement xxi
Part 1 The Commencement of Machine Learning
Solicitation to Bioinformatics 1
1 Introduction to Supervised Learning 3
Rajat Verma, Vishal Nagar and Satyasundara Mahapatra
1.1 Introduction 4
1.2 Learning Process & its Methodologies 5
1.2.1 Supervised Learning 7
1.2.2 Unsupervised Learning 8
1.2.3 Reinforcement Learning 9
1.3 Classification and its Types 10
1.4 Regression 12
1.4.1 Logistic Regression 15
1.4.2 Difference between Linear & Logistic Regression 16
1.5 Random Forest 18
1.6 K-Nearest Neighbor 20
1.7 Decision Trees 21
1.8 Support Vector Machines 22
1.9 Neural Networks 24
1.10 Comparison of Numerical Interpretation 26
1.11 Conclusion & Future Scope 27
References 28
2 Introduction to Unsupervised Learning in Bioinformatics 35
Nancy Anurag Parasa, Jaya Vinay Namgiri,
Sachi Nandan Mohanty and Jatindra Kumar Dash
2.1 Introduction 36
v
vi Contents
2.2 Clustering in Unsupervised Learning 37
2.3 Clustering in Bioinformatics—Genetic Data 38
2.3.1 Microarray Analysis 38
2.3.2 Clustering Algorithms 40
2.3.3 Partition Algorithms 41
2.3.3.1 k-Means Clustering 41
2.3.3.2 Cluster Center Initialization Algorithm (CCIA) 41
2.3.3.3 Intelligent Kernel k-Mean (IKKM) 41
2.3.3.4 Clustering Large Applications (CLARA) 42
2.3.4 Hierarchical Clustering Algorithms 42
2.3.4.1 AGNES (Agglomerative Nesting) 43
2.3.4.2 DIANA (Divisive Analysis) 43
2.3.4.3 CURE (Clustering Using Representatives) 43
2.3.4.4 CHAMELEON 43
2.3.4.5 BRICH (Balanced Iterative Reducing and
Clustering Using Hierarchies) 44
2.3.5 Density-Based Approach 44
2.3.5.1 DBSCAN 44
2.3.6 Model-Based Approach 45
2.3.6.1 SOM (Self-Organizing Maps) 45
2.3.7 Grid-Based Clustering 45
2.3.7.1 STING (Statistical Information Grid-Based
Algorithm) 46
2.3.8 Soft Clustering 46
2.3.8.1 FCM (Fuzzy Class Membership) 46
2.4 Conclusion 46
References 47
3 A Critical Review on the Application of Artificial Neural
Network in Bioinformatics 51
Vrs Jhalia and Tripti Swarnkar
3.1 Introduction 52
3.1.1 Different Areas of Application of Bioinformatics 52
3.1.2 Bioinformatics in Real World 53
3.1.3 Issues with Bioinformatics 56
3.1.3.1 Issues Related to Structure 56
3.1.3.2 Sequence Analysis 56
3.2 Biological Datasets 57
3.3 Building Computational Model 58
3.3.1 Data Pre-Processing and its Necessity 58
3.3.2 Biological Data Classification 59
Contents vii
3.3.3 ML in Bioinformatics 60
3.3.4 Introduction to ANN 61
3.3.5 Application of ANN in Bioinformatics 63
3.3.6 Broadly Used Supervised Machine Learning Techniques 64
3.4 Literature Review 64
3.4.1 Comparative Analysis of ANN With Broadly Used
Traditional ML Algorithms 67
3.5 Critical Analysis 72
3.6 Conclusion 73
References 73
Part 2 Machine Learning and Genomic Technology,
Feature Selection and Dimensionality Reduction 77
4 Dimensionality Reduction Techniques: Principles, Benefits,
and Limitations 79
Hemanta Kumar Palo, Santanu Sahoo
and Asit Kumar Subudhi
4.1 Introduction 80
4.2 The Benefits and Limitations of Dimension
Reduction Methods 81
4.3 Components of Dimension Reduction 83
4.3.1 Feature Selection 84
4.3.2 Feature Reduction 86
4.4 Methods of Dimensionality Reduction 86
4.4.1 Principal Component Analysis (PCA) 88
4.4.2 Missing Values Ratio (MVR) 89
4.4.3 Linear Discriminant Analysis (LDA) 90
4.4.4 Backward Feature Elimination (BFE) 92
4.4.5 Forward Feature Construction (FFC) 93
4.4.6 Independent Component Analysis (ICA) 94
4.4.7 Low Variance Filter (LVF) 95
4.4.8 High Correlation Filter 97
4.4.9 Random Forests (RF)/Ensemble Trees 97
4.4.10 t-Distributed Stochastic Neighbor Embedding (t-SNE) 99
4.4.11 Autoencoder 100
4.4.12 Factor Analysis (FA) 100
4.4.13 Uniform Manifold Approximation and
Projection (UMAP) 101
4.4.14 Information Gain (IG) 101
4.4.15 Vector Quantization (VQ) 102
viii Contents
4.5 Conclusion 104
References 105
5 Plant Disease Detection Using Machine Learning Tools
With an Overview on Dimensionality Reduction 109
Saurav Roy, Ratula Ray, Satya Ranjan Dash
and Mrunmay Kumar Giri
5.1 Introduction 110
5.2 Flowchart 112
5.3 Machine Learning (ML) in Rapid Stress Phenotyping 113
5.4 Dimensionality Reduction 114
5.4.1 Feature Extraction 114
5.4.1.1 PCA (Principal Component Analysis) 115
5.4.1.2 LDA (Linear Discriminant Analysis) 115
5.4.1.3 SIFT (Scale Invariant Feature Transform) 115
5.4.1.4 SURF (Speeded Up Robust Features) 116
5.4.1.5 ORB (Oriented FAST and Rotated BRIEF) 116
5.5 Literature Survey 116
5.6 Types of Plant Stress 128
5.6.1 Biotic Stress 128
5.6.1.1 Fungal Pathogen 129
5.6.1.2 Bacterial Pathogen 129
5.7 Implementation I: Numerical Dataset 130
5.7.1 Dataset Description 130
5.7.2 Results 131
5.7.3 Discussion 133
5.8 Implementation II: Image Dataset 134
5.8.1 Dataset Description 134
5.8.2 Method Used 134
5.8.3 Results 134
5.8.3.1 Results of ORB Feature Extraction
and Brute Force Matching 134
5.8.3.2 Color Histogram Comparison:
Using Correlation Method 138
5.8.4 Discussions 138
5.9 Conclusion 140
References 141
6 Gene Selection Using Integrative Analysis of Multi-Level
Omics Data: A Systematic Review 145
S. Mahapatra and T. Swarnkar
6.1 Introduction 146
Contents ix
6.2 Approaches for Gene Selection 147
6.3 Multi-Level Omics Data Integration 152
6.3.1 Horizontal Integration 153
6.3.2 Vertical Integration 153
6.4 Machine Learning Approaches for Multi-Level
Data Integration 153
6.4.1 Unsupervised Integration of Omics Data 159
6.4.2 Supervised Integration of Omics Data 163
6.5 Critical Observation 165
6.6 Conclusion 166
References 166
7 Random Forest Algorithm in Imbalance Genomics
Classification 173
Sudhansu Shekhar Patra, Om Praksah Jena, Gaurav Kumar,
Sreyashi Pramanik, Chinmaya Misra
and Kamakhya Narain Singh
7.1 Introduction 173
7.2 Methodological Issues 175
7.2.1 Decision Tree (DT) Classifier 175
7.2.2 Ensemble Techniques 177
7.2.3 Mathematical Formulation of Ensemble Technique 177
7.2.4 Bagging 178
7.2.5 Bagging Pseudocode 179
7.2.6 Random Forest 180
7.3 Biological Terminologies 181
7.3.1 DNA 181
7.3.2 Genomics 181
7.3.3 Proteins 183
7.4 Proposed Model 183
7.4.1 Balancing the Data 184
7.4.2 Ensembling of Trees 185
7.5 Experimental Analysis 186
7.6 Current and Future Scope of ML in Genomics 188
7.6.1 Gene Sequencing 188
7.6.2 Services to Consumer 188
7.6.3 Gene Editing 188
7.6.4 Pharmacy Genomics 188
7.6.5 Newborn Genetic Screening 188
7.7 Conclusion 189
References 189