Information Retrieval in Bioinformatics : A Practical Approach

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Contents 5 Editors and Contributors 7 About the Editors 7 Contributors 7 List of Figures 11 List of Tables 12 1 Bioinformatics Overviews 13 1 Background 13 2 History 14 3 Perspectives of Computer Science and Information Technology 18 4 Application of Bioinformatics 24 5 Conclusions and Future Prospects 28 References 28 2 Artificial Intelligence in Biological Sciences: A Brief Overview 31 1 Introduction 31 2 Basics of Machine Learning (ML) 33 2.1 Building Blocks of a Neural Network 33 3 How Do Neural Networks Learn? 34 4 Applications of AI in the Life Sciences 37 4.1 Using Deep Neural Networks to Understand the Biological Brain 37 4.2 AI in Medical Diagnosis 40 4.3 Decoding Protein Structure Using AI 41 4.4 AI in Ecology and Conservation Biology 42 5 Limitations of AI 43 5.1 Overfitting 43 5.2 Adversarial Examples 44 5.3 Data Bias 45 6 Conclusions 45 References 46 3 A Review of Recent Advances in Translational Bioinformatics and Systems Biomedicine 48 1 Introduction 48 2 Translational Bioinformatics 50 2.1 Categorising Translational Bioinformatics Research 51 3 Bioinformatics Interventions in Translational Research 52 3.1 Translational Biomedicine 52 3.2 Translational Clinical Research 53 3.3 Translational Stroke Research (TSR) 53 3.4 Translational Neuromedicine 53 3.5 Translational Oncology 53 3.6 Translational Imaging 54 3.7 Discovery Biology 54 3.8 Medical Biotechnology 54 3.9 Orthopaedic Transition 55 3.10 Translational Stem Cell Medicine 55 3.11 Translational Proteomics 55 3.12 Translational Neuroscience 55 3.13 Molecule Therapy 56 4 Advances in Translational Bioinformatics 56 5 Prospects of Translational Bioinformatics 57 5.1 Translational Genomics in Clinical Care 58 6 OMICs for Drug Repurposing and Discovery 59 7 Systems Biomedicine 61 7.1 Personalised Genomics 64 7.2 Genetic Testing 64 7.3 Genomic Testing for Individuals to Inform Health Care 65 7.4 Computational Health Informatics (CHI) 66 8 Related Work 67 8.1 The Ongoing Research Works Are Looking for: 68 8.2 Research Gap 69 8.3 Future Research Perspective 69 9 Conclusion 70 References 70 4 Application of Bioinformatics in Agricultural Pest Management: An Overview of the Evolving Technologies 74 1 Introduction 74 2 An Assessment of the Current State of Pest Control 75 3 Information Technology: Scope in Biological Sciences 77 4 Integrated Pest Management (IPM) Using Information and Communication Technologies (ICT) 78 5 Entomo-Informatics: A Prelude to the Concepts in Bioinformatics 79 6 Pest Control Decision Support System 80 7 Integration of Agricultural Systems and Pest Biology 81 8 Identification of the Target Protein and Its Structural Analysis 82 9 Target Pesticides and Their Virtual Screening 82 10 Computational Chemistry and Docking 83 11 Quantitative Structure–activity Relationship (QSAR) 83 12 Pest Management: Role of Mathematical Modelling 84 13 Advanced Strategies 85 14 Key Issues and Major Challenges and Their Solutions 86 15 Future Prospects 87 16 Conclusion 88 References 89 5 Application of Bioinformatics in Health Care and Medicine 94 1 Introduction 94 1.1 Major Challenges in Application of Bioinformatics 97 2 Bioinformatics and Secretome Analysis 97 3 Mass Spectrometry and Chemical Crosslinking Reagents 99 4 Bioinformatics and Software Product Line 100 4.1 Client Module 101 4.2 Core Module 102 5 Bioinformatics and Protein Kinase 102 6 Bioinformatics and miRNA 103 7 Clinical Bioinformatics and Cancer 104 8 COVID-19 Implications and Sustainable Development Goals 106 References 107 6 Information Retrieval in Bioinformatics: State of the Art and Challenges 111 1 Introduction 111 2 Literature 112 3 State of the Art 113 3.1 Information Retrieval 113 3.2 Text Categorization 114 3.3 IR in Biomedical-Informatics 114 4 Open Problems and Challenges 115 4.1 Abundant Amount of Information 115 4.2 Encoding/Preprocessing Techniques 115 4.3 Fundamental of Literature Retrieval and Mining Problems 116 4.4 Principles and Necessities for Further Standardization 116 4.5 Measurement of Existing Tools 117 5 Conclusion 118 References 118 7 Hybrid Support Vector Machine with Grey Wolf Optimization for Classifying Multivariate Data 121 1 Introduction 121 2 Literature Review 125 3 System Design 128 3.1 Support Vector Machine (SVM) Classifier: 128 3.2 Hybrid Support Vector Machine (SVM) with Grey Wolf Optimization (GWO) 130 4 Result and Discussion 132 5 Conclusion 139 References 139 8 Bioinformatics and Its Application in Computing Biological Data 143 1 Introduction 143 2 Goals 144 3 Relationship to Other Disciplines 145 4 Databases 145 4.1 The Use of Software and Tools 145 4.1.1 Bioinformatics Software that is Free and Open Source 145 4.1.2 Bioinformatics Workflow Management Systems 146 5 Applications of Bioinformatics 146 5.1 Microbial Genome Applications 147 5.1.1 Waste Cleanup 147 5.1.2 Biotechnology 148 5.1.3 Climate Change Studies 150 5.1.4 Bio-Weapon Creation 150 5.2 Medicines 152 5.2.1 Molecular Medicine 152 5.2.2 Preventative Medicine 154 5.2.3 Gene Therapy 156 5.2.4 Drug Development 156 5.2.5 Antibiotic Resistance 157 5.2.6 Evolutionary Studies 158 5.3 Agriculture 159 5.3.1 Development of Drought Resistant Varieties 159 5.3.2 Crop Improvement 160 5.3.3 Insect Resistance 160 5.3.4 Improve Nutritional Quality 161 5.4 Veterinary Science 161 6 Conclusion 162 References 162 Index 165 The book presents the results of studies on selected problems (such as predictive model of transcription initiation and termination, protein recognition codes, protein structure prediction, feature selection for disease prediction, information retrieval from medical imaging) of Bioinformatics and Information Retrieval. Information Retrieval is one of the contemporary answers to new challenges in threat evaluation of composite systems. This book provides a practical course in computational data analysis suitable for students or researchers with no previous exposure to computer programming. It describes in detail the theoretical basis for statistical analysis techniques used throughout the textbook, from basic principles. It presents walk-throughs of data analysis tasks using different tools to help in taking decisions in healthcare management.