Table of Contents
1 Cover
4 Preface
6 Part 1: THE COMMENCEMENT OF MACHINE LEARNING SOLICITATION TO BIOINFORMATICS 1 Introduction to Supervised Learning 1.1 Introduction 1.2 Learning Process & its Methodologies 1.3 Classification and its Types 1.4 Regression 1.5 Random Forest 1.6 K-Nearest Neighbor 1.7 Decision Trees 1.8 Support Vector Machines 1.9 Neural Networks 1.10 Comparison of Numerical Interpretation 1.11 Conclusion & Future Scope References 2 Introduction to Unsupervised Learning in Bioinformatics 2.1 Introduction 2.2 Clustering in Unsupervised Learning 2.3 Clustering in Bioinformatics—Genetic Data 2.4 Conclusion References 3 A Critical Review on the Application of Artificial Neural Network in Bioinformatics 3.1 Introduction 3.2 Biological Datasets 3.3 Building Computational Model 3.4 Literature Review 3.5 Critical Analysis 3.6 Conclusion References
7
Part 2: MACHINE LEARNING AND GENOMIC TECHNOLOGY, FEATURE SELECTION AND DIMENSIONALITY REDUCTION
4 Dimensionality Reduction Techniques: Principles, Benefits, and Limitations
4.1 Introduction
4.2 The Benefits and Limitations of Dimension Reduction Methods
4.3 Components of Dimension Reduction
4.4 Methods of Dimensionality Reduction
4.5 Conclusion
References
5 Plant Disease Detection Using Machine Learning Tools With an Overview on Dimensionality Reduction
5.1 Introduction
5.2 Flowchart
5.3 Machine Learning (ML) in Rapid Stress Phenotyping 113
5.4 Dimensionality Reduction
5.5 Literature Survey
5.6 Types of Plant Stress
5.7 Implementation I: Numerical Dataset
5.8 Implementation II: Image Dataset
5.9 Conclusion
References
6 Gene Selection Using Integrative Analysis of Multi-Level Omics Data: A Systematic Review
6.1 Introduction
6.2 Approaches for Gene Selection
6.3 Multi-Level Omics Data Integration
6.4 Machine Learning Approaches for Multi-Level Data Integration
6.5 Critical Observation
6.6 Conclusion
References
7 Random Forest Algorithm in Imbalance Genomics Classification
7.1 Introduction
7.2 Methodological Issues
7.3 Biological Terminologies
7.4 Proposed Model
7.5 Experimental Analysis
7.6 Current and Future Scope of ML in Genomics 188
7.7 Conclusion
References
8 Feature Selection and Random Forest Classification for Breast Cancer Disease
8.1 Introduction
8.2 Literature Survey
8.3