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Preface xvii <p>1 Integrating Genomics and Computer Vision: Unravelling Genetic Patterns and Analyzing Genomic Data 1 <br /> <i>Neha Tanwar, Sandeep Kumar, Garima Singh and Monika Bhakta</i></p> <p>2 Syndrome Detection Unleashed: Computer Vision Applications in Neurogenetic Diagnoses 25<br /> <i>R. Srilakshmi, Shilpa Choudhary, Rohit Raja and Ashish Kumar Luhach</i></p> <p>3 Integrating Machine Learning for Personalized Kidney Stone Risk Assessment: A Prospective Validation Using CLDN11 Genetic Data and Clinical Factors 59<br /> <i>Shilpa Choudhary, Monali Gulhane, Sandeep Kumar, Nitin Rakesh, Sudhanshu Maurya and Chanderdeep Tandon</i></p> <p>4 Unravelling the Complexities of Genetic Codes Through Advanced Machine Learning Algorithms for DNA Sequencing and Analysis 87<br /> <i>Swathi Gowroju, Mandeep Kumar, Sharvin Vats, Pramadvara Kushwaha and Rohit Raja</i></p> <p>5 Deciphering the Complexities of Breast Cancer: Unveiling Resistance Mechanisms 109<br /> <i>Maddula Pallavi, Chirandas Tejaswi, R. Srilakshmi and Chetan Swarup</i></p> <p>6 Deciphering the Genetic Terrain: Identifying Genetic Variants in Uncommon Disorders with Pathogenic Effects 133<br /> <i>Nikhila Kathirisetty, Ravula Arun Kumar, G. Suryanarayana, Farhana Begum, C. Padmini and Pravin Tirgar</i></p> <p>7 Genome Data-Based Explainable Recommender Systems: A State-of-the-Art Survey 149<br /> <i>V. Lakshmi Chetana and Hari Seetha</i></p> <p>8 Optimizing TCGA Data Analysis: Unveiling Crucial Cancer-Related Gene Alterations Through a Fusion Approach QL Gradient 169<br /> <i>Sushma Chowdary Polavarapu, Sri Hari Nallamala, Sudheer Mangalampalli, Brahma Naidu Nalluri, Lalitha Rajeswari Burra and Swarna Lalitha Chukka</i></p> <p>9 Leveraging Deep Learning for Genomics Analysis: Advances and Applications 191<br /> <i>Nisarg Gandhewar, Amit Pimpalkar, Anuja Jadhav, Nilesh Shelke and Rashmi Jain</i></p> <p>10 Unraveling Biological Complexity: Leveraging Deep Learning Models for Precise Classification and Understanding of Protein Types and Functions 227<br /> <i>Swathi Gowroju, M. Sudhakar, Mohit and Turki Aljrees</i></p> <p>11 The Impact of Learning Techniques on Genomics: Revolutionizing Research and Clinical Breast Cancer Application 251<br /> <i>Sumaiya Shaikh, G. Suryanarayana, ShaistaFarhat and LNC Prakash K.</i></p> <p>12 Comparison of Machine Learning and Deep Learning Algorithms for Diabetes Prediction Using DNA Sequences 269<br /> <i>Gagandeep Kaur, Poorva Agrawal, Latika Pinjarkar, Rutuja Patil, Suhashini Chaurasia and Seema Patil</i></p> <p>13 AI Applications in Analyzing Gene Expression for Cancer Diagnosis: A Comprehensive Review 285<br /> <i>Poorva Agrawal, Gagandeep Kaur, Vansh Gupta, Kruthika Agarwal, Latika Pinjarkar and Seema Patil</i></p> <p>14 Optimum Detection of Human Genome Related to Cancer Cells Using Signal Processing 309 <br /> <i>Manoranjan Dash and Ritesh Raj</i></p> <p>15 Genomics-Driven Strategies for Sustainable Crop Improvement in Agriculture 321<br /> <i>Munish Kumar, Monika Kajal, Mandeep Kumar, Ramesh Kumar and Pramadvara Kushwaha</i></p> <p>16 An Efficient Deep Convolutional Neural Networks Model for Genomic Sequence Classification 345<br /> <i>Amit Pimpalkar, Nisarg Gandhewar, Nilesh Shelke, Sachin Patil and Sharda Chhabria</i></p> <p>17 Navigating the Genetic Tapestry Using Genetic Analysis on the SLC26A1 Gene Variants in the Detection and Understanding of Kidney Stones for Improved Global Healthcare Management 377<br /> <i>Sandeep Kumar, Monali Gulhane, Nitin Rakesh, Sudhanshu Maurya, Rajni Mohana and Chanderdeep Tandon</i></p> <p>18 A Comprehensive Approach for Enhancing Kidney Disease Detection Using Random Forest and Gradient Boosting 395<br /> <i>Mandeep Kumar, Neerav Khare, Soumya Mani, Monika Bhaktaand Gaurab Saha</i></p> <p>19 Decoding the Future: COVID-19 RNA Sequence Prediction Through LSTM Transformation 417<br /> <i>M.D. Khaja Shaik, K. Narsimhulu, B.V.N. Praveena,Sarita Dabur and G. Pratyusha</i></p> <p>20 Genomics and Machine Learning: ML Approaches, Future Directions and Challenges in Genomics 437<br /> <i>Sunita Gupta, Neha Janu, Meenakshi Nawal and Anjali Goswami</i></p> <p>21 Predicting Gene Ontology Annotations from CAFA Using Distance Machine Learning and Transfer Metric Learning 459<br /> <i>Shilpa Choudhary, MD Khaja Shaik, Sivaneasan Bala Krishnan and Sunita Gupta</i></p> <p>22 PacMan-RL: A Game-Changing Approach to Drug Development Through Reinforcement Learning 483<br /> <i>Abhishek Goud Amkamgari, Harshita Sharma, Rashmi Verma and Bhawna Kaliraman</i></p> <p>23 Genetic Variant Classification Through Decision Tree Analysis for Enhanced Genomic Understanding 505<br /> <i>Prachi Chaudhary and Rajni Mehra</i></p> <p>Index 529</p>

<p><b>Shilpa Choudhary, PhD,</b> is a postdoctoral fellow at the Singapore Institute of Technology, Singapore. She has authored more than 50 research papers in various national and international journals as well as authored/edited four books. She has been awarded nine patents and was awarded the ‘Gold Medal’ in 2012. <p><b>Sandeep Kumar, PhD,</b> is a professor in the Department of Computer Science and Engineering, K L Deemed to be University, Vijayawada, Andhra Pradesh, India. He has been granted six patents and has successfully filed another ten. He has published more than 100 research papers in various national and international journals and conferences. <p><b>Swathi Gowroju, PhD,</b> is an associate professor and deputy head of the Data Science Department, Sretas Institute of Engineering and Technology, Hyderabad, Telangana, India. She has published more than 30 research papers in the fields of image processing and machine learning. <p><b>Monali Gulhane, PhD,</b> is an assistant professor at the Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India. She received the ‘Young Researcher Award’ in 2022. <p><b>R. Sri Lakshmi, PhD,</b> is a postdoctoral fellow at the Singapore Institute of Technology, Singapore. She is proficient in machine learning, artificial intelligence, computer design, etc. Most of her research has been published in renowned journals, patents, and book chapters.

<p><b>The book provides a comprehensive understanding of cutting-edge research and applications at the intersection of genomics and advanced AI techniques and serves as an essential resource for researchers, bioinformaticians, and practitioners looking to leverage genomics data for AI-driven insights and innovations.</b> <p>The book encompasses a wide range of topics, starting with an introduction to genomics data and its unique characteristics. Each chapter unfolds a unique facet, delving into the collaborative potential and challenges that arise from advanced technologies. It explores image analysis techniques specifically tailored for genomic data. It also delves into deep learning showcasing the power of convolutional neural networks (CNN) and recurrent neural networks (RNN) in genomic image analysis and sequence analysis. Readers will gain practical knowledge on how to apply deep learning techniques to unlock patterns and relationships in genomics data. Transfer learning, a popular technique in AI, is explored in the context of genomics, demonstrating how knowledge from pre-trained models can be effectively transferred to genomic datasets, leading to improved performance and efficiency. Also covered is the domain adaptation techniques specifically tailored for genomics data. The book explores how genomics principles can inspire the design of AI algorithms, including genetic algorithms, evolutionary computing, and genetic programming. Additional chapters delve into the interpretation of genomic data using AI and ML models, including techniques for feature importance and visualization, as well as explainable AI methods that aid in understanding the inner workings of the models. The applications of genomics in AI span various domains, and the book explores AI-driven drug discovery and personalized medicine, genomic data analysis for disease diagnosis and prognosis, and the advancement of AI-enabled genomic research. Lastly, the book addresses the ethical considerations in integrating genomics with AI, computer vision, and machine learning. <p><b>Audience</b> <p>The book will appeal to biomedical and computer/data scientists and researchers working in genomics and bioinformatics seeking to leverage AI, computer vision, and machine learning for enhanced analysis and discovery; healthcare professionals advancing personalized medicine and patient care; industry leaders and decision-makers in biotechnology, pharmaceuticals, and healthcare industries seeking strategic insights into the integration of genomics and advanced technologies.

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