Genomics at the Nexus of AI, Computer Vision, and Machine Learning

Shilpa Choudhary, Sandeep Kumar, Swathi Gowroju, Monali Gulhane, R. Sri Lakshmi

• Publisher: Wiley-Scrivener
• Year: 2024
• Print ISBN: 9781394268801
• eBook ISBN: 9781394268825
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Graphics
• Index: Computer Science

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.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Chapter 1 Integrating Genomics and Computer Vision: Unravelling Genetic Patterns and Analyzing Genomic Data
• Chapter 2 Syndrome Detection Unleashed: Computer Vision Applications in Neurogenetic Diagnoses
• Chapter 3 Integrating Machine Learning for Personalized Kidney Stone Risk Assessment: A Prospective Validation Using CLDN11 Genetic Data and Clinical Factors
• Chapter 4 Unravelling the Complexities of Genetic Codes Through Advanced Machine Learning Algorithms for DNA Sequencing and Analysis
• Chapter 5 Deciphering the Complexities of Breast Cancer: Unveiling Resistance Mechanisms
• Chapter 6 Deciphering the Genetic Terrain: Identifying Genetic Variants in Uncommon Disorders with Pathogenic Effects
• Chapter 7 Genome Data-Based Explainable Recommender Systems: A State-of-the-Art Survey
• Chapter 8 Optimizing TCGA Data Analysis: Unveiling Crucial Cancer-Related Gene Alterations Through a Fusion Approach QL Gradient
• Chapter 9 Leveraging Deep Learning for Genomics Analysis: Advances and Applications
• Chapter 10 Unraveling Biological Complexity: Leveraging Deep Learning Models for Precise Classification and Understanding of Protein Types and Functions
• Chapter 11 The Impact of Learning Techniques on Genomics: Revolutionizing Research and Clinical Breast Cancer Application
• Chapter 12 Comparison of Machine Learning and Deep Learning Algorithms for Diabetes Prediction Using DNA Sequences
• Chapter 13 AI Applications in Analyzing Gene Expression for Cancer Diagnosis: A Comprehensive Review
• Chapter 14 Optimum Detection of Human Genome Related to Cancer Cells Using Signal Processing
• Chapter 15 Genomics-Driven Strategies for Sustainable Crop Improvement in Agriculture
• Chapter 16 An Efficient Deep Convolutional Neural Networks Model for Genomic Sequence Classification
• Chapter 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
• Chapter 18 A Comprehensive Approach for Enhancing Kidney Disease Detection Using Random Forest and Gradient Boosting
• Chapter 19 Decoding the Future: COVID-19 RNA Sequence Prediction Through LSTM Transformation
• Chapter 20 Genomics and Machine Learning: ML Approaches, Future Directions and Challenges in Genomics
• Chapter 21 Predicting Gene Ontology Annotations from CAFA Using Distance Machine Learning and Transfer Metric Learning
• Chapter 22 PacMan-RL: A Game-Changing Approach to Drug Development Through Reinforcement Learning
• Chapter 23 Genetic Variant Classification Through Decision Tree Analysis for Enhanced Genomic Understanding
• Index
• EULA