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 1.3 Classification and its Types 10 1.4 Regression 12 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 2.2 Clustering in Unsupervised Learning 37 2.3 Clustering in Bioinformatics—Genetic Data 38 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 3.2 Biological Datasets 57 3.3 Building Computational Model 58 3.4 Literature Review 64 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 4.4 Methods of Dimensionality Reduction 86 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 5.5 Literature Survey 116 5.6 Types of Plant Stress 128 5.7 Implementation I: Numerical Dataset 130 5.8 Implementation II: Image Dataset 134 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 6.2 Approaches for Gene Selection 147 6.3 Multi-Level Omics Data Integration 152 6.4 Machine Learning Approaches for Multi-Level Data Integration 153 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 7.3 Biological Terminologies 181 7.4 Proposed Model 183 7.5 Experimental Analysis 186 7.6 Current and Future Scope of ML in Genomics 188 7.7 Conclusion 189 References 189 8 Feature Selection and Random Forest Classification for Breast Cancer Disease 191 Shubham Raj, Swati Singh, Avinash Kumar, Sobhangi Sarkar and Chittaranjan Pradhan 8.1 Introduction 192 8.2 Literature Survey 192 8.3 Machine Learning 196 8.4 Feature Engineering 202 8.5 Methodology 204 8.6 Result Analysis 209 8.7 Conclusion 210 References 210 9 A Comprehensive Study on the Application of Grey Wolf Optimization for Microarray Data 211 Swati Sucharita, Barnali Sahu and Tripti Swarnkar 9.1 Introduction 212 9.2 Microarray Data 213 9.3 Grey Wolf Optimization (GWO) Algorithm 214 9.4 Studies on GWO Variants 220 9.5 Application of GWO in Medical Domain 232 9.6 Application of GWO in Microarray Data 232 9.7 Conclusion and Future Work 232 References 243 10 The Cluster Analysis and Feature Selection: Perspective of Machine Learning and Image Processing 249 Aradhana Behura 10.1 Introduction 251 10.2 Various Image Segmentation Techniques 254 10.3 How to Deal With Image Dataset 256 10.4 Class Imbalance Problem 264 10.5 Optimization of Hyperparameter 267 10.6 Case Study 270 10.7 Using AI to Detect Coronavirus 273 10.8 Using Artificial Intelligence (AI), CT Scan and X-Ray 274 10.9 Conclusion 276 References 276 Part 3 Machine Learning and Healthcare Applications 281 11 Artificial Intelligence and Machine Learning for Healthcare Solutions 283 Ashok Sharma, Parveen Singh and Gowhar Dar 11.1 Introduction 284 11.2 Using Machine Learning Approaches for Different Purposes 284 11.3 Various Resources of Medical Data Set for Research 286 11.4 Deep Learning in Healthcare 287 11.5 Various Projects in Medical Imaging and Diagnostics 288 11.6 Conclusion 289 References 290 12 Forecasting of Novel Corona Virus Disease (Covid-19) Using LSTM and XG Boosting Algorithms 293 V. Aakash, S. Sridevi, G. Ananthi and S. Rajaram 12.1 Introduction 294 12.2 Machine Learning Algorithms for Forecasting 296 12.3 Proposed Method 300 12.4 Implementation 304 12.5 Results and Discussion 307 12.6 Conclusion and Future Work 310 References 310 13 An Innovative Machine Learning Approach to Diagnose Cancer at Early Stage 313 Poongodi, P., Udayakumar, E., Srihari, K. and Sachi Nandan Mohanty 13.1 Introduction 314 13.2 Related Work 317 13.3 Materials and Methods 320 13.4 System Design 322 13.5 Results and Discussion 331 13.6 Conclusion 335 References 335 14 A Study of Human Sleep Staging Behavior Based on Polysomnography Using Machine Learning Techniques 339 Santosh Kumar Satapathy and D. Loganathan 14.1 Introduction 340 14.2 Polysomnography Signal Analysis 341 14.3 Case Study on Automated Sleep Stage Scoring 349 14.4 Summary and Conclusion 356 References 357 15 Detection of Schizophrenia Using EEG Signals 359 Shalini Mahato, Laxmi Kumari Pathak and Kajal Kumari 15.1 Introduction 360 15.2 Methodology 367 15.3 Literature Review 372 15.4 Discussion 372 15.5 Conclusion 388 References 388 16 Performance Analysis of Signal Processing Techniques in Bioinformatics for Medical Applications Using Machine Learning Concepts 391 G. Aparna, G. Anitha Mary and G. Sumana 16.1 Introduction 392 16.2 Basic Definition of Anatomy and Cell at Micro Level 397 16.3 Signal Processing—Genome Signal Processing 403 16.4 Hotspots Identification Algorithm 414 16.5 Results—Experimental Investigations 416 16.6 Analysis Using Machine Learning Metrics 418 16.7 Conclusion 424 Appendix 424 A.1 Hotspot Identification Code 424 A.2 Performance Metrics Code 425 References 427 17 Survey of Various Statistical Numerical and Machine Learning Ontological Models on Infectious Disease Ontology 431 Yuvaraj Natarajan, Srihari Kannan and Sachi Nandan Mohanty 17.1 Introduction 432 17.2 Disease Ontology 432 17.3 Infectious Disease Ontology 433 17.4 Biomedical Ontologies on IDO 434 17.5 Various Methods on IDO 435 17.6 Machine Learning-Based Ontology for IDO 436 17.7 Recommendation or Suggestions for Future Study 437 17.8 Conclusions 438 References 438 18 An Efficient Model for Predicting Liver Disease Using Machine Learning 443 Ritesh Choudhary, T. Gopalakrishnan, D. Ruby, A. Gayathri, Vishnu Srinivasa Murthy and Rishabh Shekhar 18.1 Introduction 444 18.2 Related Works 445 18.3 Proposed Model 446 18.4 Results and Analysis 454 18.5 Conclusion 456 References 456 Part 4 Bioinformatics and Market Analysis 459 19 A Novel Approach for Prediction of Stock Market Behavior Using Bioinformatics Techniques 461 Prakash Kumar Sarangi, Birendra Kumar Nayak and Sachidananda Dehuri 19.1 Introduction 462 19.2 Literature Review 463 19.3 Proposed Work 466 19.4 Experimental Study 470 19.5 Conclusion and Future Work 482 References 484 20 Stock Market Price Behavior Prediction Using Markov Models: A Bioinformatics Approach 485 Prakash Kumar Sarangi, Birendra Kumar Nayak and Sachidananda Dehuri 20.1 Introduction 486 20.2 Literature Survey 487 20.3 Proposed Work 488 20.4 Experimental Work 497 20.5 Conclusions and Future Work 504 References 505 Index 507

The chapters are based on progressive collaborative research work on a broad range of topics and implementations, and will be of interest to both researchers and students from computer science and biological domains. Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Data Analytics in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data, and much more. Audience The book is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.