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Genomics at the Nexus of AI, Computer Vision, and Machine Learning
Shilpa Choudhary (Edited by), S Choudhary (Author), Sandeep Kumar (Edited by), Swathi Gowroju (Edited by), Monali Gulhane (Edited by), R. Sri Lakshmi (Edited by)
9781394268801, Wiley
Hardback, published 22 December 2024
560 pages
25 x 15 x 1.5 cm, 0.454 kg
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. 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. Audience 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.
Preface xvii 1 Integrating Genomics and Computer Vision: Unravelling Genetic Patterns and Analyzing Genomic Data 1 2 Syndrome Detection Unleashed: Computer Vision Applications in Neurogenetic Diagnoses 25 3 Integrating Machine Learning for Personalized Kidney Stone Risk Assessment: A Prospective Validation Using CLDN11 Genetic Data and Clinical Factors 59 4 Unravelling the Complexities of Genetic Codes Through Advanced Machine Learning Algorithms for DNA Sequencing and Analysis 87 5 Deciphering the Complexities of Breast Cancer: Unveiling Resistance Mechanisms 109 6 Deciphering the Genetic Terrain: Identifying Genetic Variants in Uncommon Disorders with Pathogenic Effects 133 7 Genome Data-Based Explainable Recommender Systems: A State-of-the-Art Survey 149 8 Optimizing TCGA Data Analysis: Unveiling Crucial Cancer-Related Gene Alterations Through a Fusion Approach QL Gradient 169 9 Leveraging Deep Learning for Genomics Analysis: Advances and Applications 191 10 Unraveling Biological Complexity: Leveraging Deep Learning Models for Precise Classification and Understanding of Protein Types and Functions 227 11 The Impact of Learning Techniques on Genomics: Revolutionizing Research and Clinical Breast Cancer Application 251 12 Comparison of Machine Learning and Deep Learning Algorithms for Diabetes Prediction Using DNA Sequences 269 13 AI Applications in Analyzing Gene Expression for Cancer Diagnosis: A Comprehensive Review 285 14 Optimum Detection of Human Genome Related to Cancer Cells Using Signal Processing 309 15 Genomics-Driven Strategies for Sustainable Crop Improvement in Agriculture 321 16 An Efficient Deep Convolutional Neural Networks Model for Genomic Sequence Classification 345 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 18 A Comprehensive Approach for Enhancing Kidney Disease Detection Using Random Forest and Gradient Boosting 395 19 Decoding the Future: COVID-19 RNA Sequence Prediction Through LSTM Transformation 417 20 Genomics and Machine Learning: ML Approaches, Future Directions and Challenges in Genomics 437 21 Predicting Gene Ontology Annotations from CAFA Using Distance Machine Learning and Transfer Metric Learning 459 22 PacMan-RL: A Game-Changing Approach to Drug Development Through Reinforcement Learning 483 23 Genetic Variant Classification Through Decision Tree Analysis for Enhanced Genomic Understanding 505 Index 529
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Subject Areas: Computer science [UY]
