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Artificial intelligence is transforming the life sciences. From decoding genomic data and predicting protein structures to advancing precision medicine and digital healthcare, AI is reshaping how researchers investigate biological systems, understand disease, discover new therapies, and improve patient care.

This book explores the convergence of artificial intelligence, machine learning, molecular biology, biomedical research, and healthcare. This volume provides the essential foundations required to understand one of the most significant scientific revolutions of the twenty-first century.

As advances in genomics, transcriptomics, proteomics, metabolomics, medical imaging, and electronic health records generate unprecedented amounts of biological data, traditional analytical approaches are no longer sufficient. Artificial intelligence enables researchers and healthcare professionals to extract meaningful insights from complex datasets, accelerating scientific discovery and clinical innovation.

Table of Contents

Dedication

About the Series

Other Books by the Author

Preface

About This Book

Additional Learning Resources and Related Resources

Who This Book Is For

How to Use This Book

Abbreviations and Terminology

PART I: FOUNDATIONS OF ARTIFICIAL INTELLIGENCE IN LIFE SCIENCES

Chapter 1: The Convergence of AI, Biology, and Medicine

Introduction

1.1 The Biological Data Revolution

1.2 Why AI Matters in Modern Biology

1.3 Historical Development of AI in Healthcare

1.4 Current Applications and Future Directions

1.5 Challenges and Opportunities

Chapter 1 References

Chapter 2: Biological Data and Digital Medicine

Introduction

2.1 Types of Biological Data

2.2 Genomic Data

2.3 Transcriptomic Data

2.4 Proteomic Data

2.5 Metabolomic Data

2.6 Clinical and Imaging Data

2.7 Electronic Health Records

Chapter 2 References

Chapter 3: Fundamentals of Machine Learning

Introduction

3.1 What Is Artificial Intelligence?

3.2 Fundamentals of Machine Learning

3.3 Supervised Learning\t104

3.4 Unsupervised Learning

3.5 Reinforcement Learning

3.6 Model Evaluation and Performance Metrics

3.7 Overfitting, Underfitting, and Generalization

3.8 Opportunities and Limitations of Machine Learning

Chapter 3 References

Chapter 4: Deep Learning and Neural Networks

Introduction\t129

4.1 Foundations of Deep Learning

4.2 Deep Neural Networks and Representation Learning

4.3 Major Deep Learning Architectures\t139

4.4 Deep Learning Applications in Biology and Medicine

Chapter 4 References

PART II: AI IN MOLECULAR BIOLOGY

Chapter 5: AI for DNA Sequence Analysis

Introduction

5.1 DNA as Information

5.2 Pattern Recognition in Genomes

5.3 Sequence Classification

5.4 Variant Detection

5.5 Mutation Prediction

5.6 Functional Annotation

Chapter 5 References

Chapter 6: AI in Genomics

Introduction

6.1 Genome Assembly

6.2 Genome Annotation

6.3 Comparative Genomics

6.4 Population Genomics

6.5 Rare Disease Genomics

6.6 Cancer Genomics

Chapter 6 References

Chapter 7: AI in Transcriptomics

Introduction

7.1 RNA Sequencing

7.2 Gene Expression Analysis

7.3 Differential Expression

7.4 Single-Cell Transcriptomics

7.5 Spatial Transcriptomics

7.6 Transcriptome Prediction

Chapter 7 References

Chapter 8: AI in Proteomics

Introduction

8.1 Protein Structure Prediction

8.2 Protein Function Prediction

8.3 Protein Interaction Networks

8.4 Proteome Mapping

8.5 Clinical Proteomics

Chapter 8 References

About the Author

Companion Resource Library

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