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

Edited by Shilpa Choudhary, Sandeep Kumar, Swathi Gowroju, Monali Gulhane and R. Sri Lakshmi
Copyright: 2024   |   Expected Pub Date:2024/09/30
ISBN: 9781394268801  |  Hardcover  |  
552 pages

One Line Description
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.

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.

Description
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.

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Author / Editor Details
Shilpa Choudhary, PhD, 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.

Sandeep Kumar, PhD, 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.

G. Swathi, PhD, 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.

Monali Gulhane, PhD, 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.

R. Sri Lakshmi, PhD, 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.

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Table of Contents
Preface
1. Integrating Genomics and Computer Vision: Unravelling Genetic Patterns and Analyzing Genomic Data
Neha Tanwar, Sandeep Kumar, Garima Singh and Monika Bhakta
1.1 Introduction
1.2 Computer Vision in Genomic Research
1.3 Image Analysis Techniques for Genomic Data
1.3.1 Preprocessing Techniques
1.3.2 Segmentation Techniques
1.3.3 Feature Detection and Extraction
1.3.4 Classification Techniques
1.4 A Journey Through Computer Vision for Detecting and Analyzing Genetic Patterns
1.5 Case Study
1.6 Applications of Image Analysis in Genomic Research
1.7 Challenges Involved in Analyzing Images for Genomic Data in Computer Vision
1.8 Conclusion
References
2. Syndrome Detection Unleashed: Computer Vision Applications in Neurogenetic Diagnoses
R. Srilakshmi, Shilpa Choudhary, Rohit Raja and Ashish Kumar Luhach
2.1 Introduction
2.1.1 An Insight Into the Complexity of the Genome Can Be Gained Through Sequence Analysis
2.1.2 The Single-Cell Genomics Approach: A Step Towards Understanding Cellular Heterogeneity
2.1.3 Putting Genes and Geographical Information Together Through Spatial Transcriptomics
2.1.4 The Ability to Recognize Illnesses Through Disease Detection and Diagnosis
2.1.5 The Role of Computer Vision in Drug Discovery: Alchemy in the Digital Age
2.1.6 The Foundation of Genomic Computer Vision is Presented by Machine Learning and Deep Learning
2.1.7 Manifestations of Craniosynostosis
2.1.8 The Diagnosis of Cranial Synostosis
2.1.9 Categories of Craniosynostosis
2.1.10 Synostosis with a Single Future (Primary Kind)
2.1.11 A Synostosis of the Double-Suture
2.1.12 Complicated Synostosis of Multiple Futures
2.1.13 Surgery to Treat Craniosynostosis with Minimal Invasiveness
2.2 Related Work
2.3 Proposed Methodology
2.4 Results and Discussion
2.4.1 Loading the Dataset and Training the Model
2.4.2 Label Correlogram
2.5 Conclusion and Future Scope
References
3. Integrating Machine Learning for Personalized Kidney Stone Risk Assessment: A Prospective Validation Using CLDN11 Genetic Data and Clinical Factors
Shilpa Choudhary, Monali Gulhane, Sandeep Kumar, Nitin Rakesh, Sudhanshu Maurya and Chanderdeep Tandon
3.1 Introduction
3.2 Literature Survey
3.3 Proposed Methodology
3.3.1 Data Preprocessing
3.3.2 Feature Selection
3.3.3 Classification Using Different Machine Learning Algorithms
3.3.4 Prediction Evaluation
3.3.5 Kidney Stone Risk Prediction
3.4 Results and Discussions
3.4.1 Model Training
3.4.2 Model Evaluation
3.5 Conclusion and Future Work
References
4. Unravelling the Complexities of Genetic Codes Through Advanced Machine Learning Algorithms for DNA Sequencing and Analysis
Swathi Gowroju, Mandeep Kumar, Sharvin Vats, Pramadvara Kushwaha and Rohit Raja
4.1 Introduction
4.2 Literature Survey
4.3 Proposed Method
4.3.1 Preprocessing
4.3.2 DNA Sequence Data Using Ordinal Encoding
4.3.3 DNA Sequence Using One-Hot Encoding
4.3.4 Using k-mer Counting
4.3.5 Dataset
4.3.6 Model Designing
4.3.7 Training
4.4 Results
4.5 Conclusion
References
5. Deciphering the Complexities of Breast Cancer: Unveiling Resistance Mechanisms
Maddula Pallavi, Chirandas Tejaswi, R. Srilakshmi and Chetan Swarup
5.1 Introduction
5.2 Literature Review
5.3 Proposed Methodology
5.3.1 Preprocessing
5.3.2 Classification Using Different Algorithms
5.4 Results
5.4.1 Dataset
5.4.2 Performance Evaluation Parameters
5.5 Conclusion and Future Scope
References
6. Deciphering the Genetic Terrain: Identifying Genetic Variants in Uncommon Disorders with Pathogenic Effects
Nikhila Kathirisetty, Ravula Arun Kumar, G. Suryanarayana, Farhana Begum, C. Padmini and Pravin Tirgar
6.1 Introduction
6.2 Literature Survey
6.3 Methodology
6.3.1 Data Acquisition
6.3.2 Preprocessing
6.3.3 Statistical Analysis
6.3.4 Data Availability
6.4 Whole Exome Sequencing (WES) with Copy Number Variation (CNV) Analysis
6.5 Results and Analysis
6.5.1 Evaluation Parameters Used
6.5.2 Analysis of the Proposed Work
6.5.2.1 Genetic Variant Identification
6.5.2.2 Disease-Causing Variant Identification
6.5.2.3 Variant Frequency and Inheritance Patterns
6.5.3 Functional Characterization Results
6.5.4 Operational Consequences of the Variants
6.6 Conclusion
References
7. Genome Data-Based Explainable Recommender Systems: A State-of-the-Art Survey
V. Lakshmi Chetana and Hari Seetha
7.1 Introduction
7.1.1 Explainable AI
7.1.2 Explanation Types
7.1.3 Explanation Scope
7.1.4 Model Agnosticity
7.1.5 Techniques for Model Explanation
7.1.6 Explainable Genome Recommendation Systems
7.2 Literature Survey
7.3 Challenges of Explainable Genome Recommendation Systems
7.4 Future Directions of Explainable Genome Recommendation Systems
7.5 Case Study: Explainable Genome Recommendation Systems for Cancer Treatment
7.6 Conclusion
References
8. Optimizing TCGA Data Analysis: Unveiling Crucial Cancer-Related Gene Alterations Through a Fusion Approach QL Gradient
Sushma Chowdary Polavarapu, Sri Hari Nallamala, Sudheer Mangalampalli, Brahma Naidu Nalluri, Lalitha Rajeswari Burra and Swarna Lalitha Chukka
8.1 Introduction
8.1.1 Identification and Classification of Coronavirus Genomic Signals Based on Linear Predictive Coding and Machine Learning Methods
8.1.2 A Machine Learning Approach Based on ACMG/AMP Guidelines for Genomic Variant Classification and Prioritization
8.1.3 Different Types of Quantum Algorithms
8.2 Literature Survey
8.3 Proposed Methodology
8.3.1 Feature Engineering Using PCA and Correlation
8.3.2 Integrated Quantum with Gradient Boost
8.4 Results and Discussion
8.4.1 Materials Utilized
8.5 Conclusion and Future Work
References
9. Leveraging Deep Learning for Genomics Analysis: Advances and Applications
Nisarg Gandhewar, Amit Pimpalkar, Anuja Jadhav, Nilesh Shelke and Rashmi Jain
9.1 Introduction
9.1.1 Genomics Analysis
9.1.2 Significance of Advancing Genomics Analysis Research
9.2 Genomics Data Types
9.2.1 DNA Sequencing
9.2.2 RNA Sequencing
9.2.3 Epigenetic Data
9.2.4 Metagenomics
9.3 State-of-the-Art Deep Learning Models for Genomics Analysis
9.3.1 Machine and Deep Learning
9.3.2 Supervised Learning in Genomics Analysis
9.3.3 Unsupervised Learning in Genomics Analysis
9.3.4 Attention Mechanisms in Genomics
9.3.5 Reinforcement Learning in Genomics
9.3.6 Transformers in Genomics Analysis
9.4 Importance of Data Preprocessing and Cleaning in Genomics Analysis
9.4.1 Techniques for Handling Missing Data
9.4.2 Normalization in Genomics Data
9.4.3 Feature Selection in Genomics Data
9.4.4 Cross-Validation in Genomics Analysis
9.4.5 Hyperparameter Tuning for Deep Learning Models
9.5 Applications of Deep Learning in Genomics Analysis
9.5.1 DNA Sequencing
9.5.2 Gene Function Prediction
9.5.2.1 Data Representation
9.5.2.2 Model Architecture
9.5.2.3 Transfer Learning
9.5.2.4 Integration of Multiple Data Types
9.5.2.5 Attention Mechanisms
9.5.2.6 Multi-Modal Approaches
9.5.3 Gene Regulation
9.5.4 Personalized Medicine
9.5.5 Drug Discovery and Genomics
9.5.5.1 Drug–Drug Target Interactions Prediction
9.5.5.2 Drug Sensitivity and Responsiveness
9.5.5.3 Drug Side Effect Predictions
9.5.5.4 Drug–Drug Similarity Prediction
9.5.6 Cancer Genomics
9.6 Challenges in Using Deep Learning in Genomics
9.7 Conclusion
9.8 Future Directions
References
10. Unraveling Biological Complexity: Leveraging Deep Learning Models for Precise Classification and Understanding of Protein Types and Functions
Swathi Gowroju, M. Sudhakar, Mohit and Turki Aljrees
10.1 Introduction
10.2 Literature Work
10.3 Proposed Methodology
10.4 Results
References
11. The Impact of Learning Techniques on Genomics: Revolutionizing Research and Clinical Breast Cancer Application
Sumaiya Shaikh, G. Suryanarayana, ShaistaFarhat and LNC Prakash K.
11.1 Introduction
11.2 Literature Survey
11.3 Proposed Methodology
11.3.1 Abbreviated Language of Biological Information
11.3.2 Biochemical Identity or Similarity
11.3.3 Mapping Genetic Sequence Onto Cancer Cells
11.3.4 Reverse Complement
11.4 Conclusion
11.5 Future Scope
References
12. Comparison of Machine Learning and Deep Learning Algorithms for Diabetes Prediction Using DNA Sequences
Gagandeep Kaur, Poorva Agrawal, Latika Pinjarkar, Rutuja Patil, Suhashini Chaurasia and Seema Patil
12.1 Introduction
12.2 Literature Survey
12.2.1 Diabetes Prediction Using ML Approaches
12.2.2 Diabetes Prediction Using DL-Based Techniques
12.3 Proposed Methodology
12.3.1 Dataset Description
12.3.2 Data Preprocessing
12.3.3 Feature Engineering
12.3.4 ML- and DL-Based Classifiers
12.4 Experimental Results
12.5 Conclusion
References
13. AI Applications in Analyzing Gene Expression for Cancer Diagnosis: A Comprehensive Review
Poorva Agrawal, Gagandeep Kaur, Vansh Gupta, Kruthika Agarwal, Latika Pinjarkar and Seema Patil
13.1 Introduction
13.2 Expression of Gene Data
13.2.1 The Microarray Data
13.2.2 RNA-Seq Data
13.3 Feature Selection Methods for Gene Expression Analysis
13.3.1 Filter Methods
13.3.2 Wrapper Method
13.3.3 Embedded Methods
13.4 ML/DL Methods for Gene Expression Analysis
13.4.1 Machine Learning (ML)
13.4.2 Deep Learning (DL)
13.4.3 Transfer Learning (TL)
13.5 Graph Analysis
13.6 Conclusion
References
14. Optimum Detection of Human Genome Related to Cancer Cells Using Signal Processing
Manoranjan Dash and Ritesh Raj
14.1 Introduction
14.2 Methodology
14.3 Results and Discussion
14.4 Conclusion
References
15. Genomics-Driven Strategies for Sustainable Crop Improvement in Agriculture
Munish Kumar, Monika Kajal, Mandeep Kumar, Ramesh Kumar and Pramadvara Kushwaha
15.1 Introduction
15.2 Related Work
15.3 Problem Statement
15.4 Proposed Model
15.5 Results and Discussion
15.6 Conclusion and Future Scope
References
16. An Efficient Deep Convolutional Neural Networks Model for Genomic Sequence Classification
Amit Pimpalkar, Nisarg Gandhewar, Nilesh Shelke, Sachin Patil and Sharda Chhabria
16.1 Introduction
16.1.1 Convolutional Neural Network in Genomics
16.1.2 The Architecture of CNN
16.1.3 Layers of CNN
16.1.4 Recurrent Neural Networks in Genomics
16.1.5 The Role of Deep Learning in Genomics
16.2 Case Study
16.2.1 Enhancing Genomic Variant Classification with CNN
16.3 Results
16.4 Limitations of Deep Learning in Genomics
16.5 Conclusion and Future Directions
References
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
Sandeep Kumar, Monali Gulhane, Nitin Rakesh, Sudhanshu Maurya, Rajni Mohana and Chanderdeep Tandon
17.1 Introduction
17.1.1 Discussion
17.2 Literature Review
17.3 Analysis of SLC26A1 Gene for Kidney Stone Prediction
17.4 Functions of SLC26A1
17.5 Categories of Confidence
17.6 Conclusion
References
18. A Comprehensive Approach for Enhancing Kidney Disease Detection Using Random Forest and Gradient Boosting
Mandeep Kumar, Neerav Khare, Soumya Mani, Monika Bhakta and Gaurab Saha
18.1 Introduction
18.2 Literature Survey
18.3 Problem Statement
18.4 Proposed Methodology
18.5 Experimental Results and Analysis
18.5.1 Description of Dataset
18.5.2 Performance Parameters
18.5.3 Resource Usage
18.5.4 Comparison with Other ML Techniques
18.6 Conclusion
References
19. Decoding the Future: COVID‑19 RNA Sequence Prediction Through LSTM Transformation
M.D. Khaja Shaik, K. Narsimhulu, B.V.N. Praveena, Sarita Dabur and G. Pratyusha
19.1 Introduction
19.2 Literature Survey
19.2.1 Viruses of DNA
19.2.2 RNA-Based Viruses
19.3 Proposed System
19.3.1 Data Preparation
19.3.2 Time Series Model
19.3.3 Classification Algorithm
19.4 Experimental Setup and Discussion
19.4.1 Dataset: Human Genome Sequences Dataset
19.4.2 Analysis of Dataset
19.4.3 Training
19.4.4 Adding Transformers to the Model
19.5 Conclusion and Future Scope
References
20. Genomics and Machine Learning: ML Approaches, Future Directions and Challenges in Genomics
Sunita Gupta, Neha Janu, Meenakshi Nawal and Anjali Goswami
20.1 Introduction
20.1.1 Genomics Data
20.1.2 DNA Sequencing
20.1.3 RNA Sequence
20.1.4 Protein Sequences
20.2 Unique Characteristics of Genomics Data
20.3 Significance of Genomics Data in AI and ML
20.4 ML Approaches Applied in Genomics Research and Their Applications
20.5 Contributions to ML Approaches in Genomic Data Analysis
20.6 Gene Expression Prediction and Disease Classification Using ML
20.7 Challenges in Genomics
20.8 Future Directions in Genomics
References
21. Predicting Gene Ontology Annotations from CAFA Using Distance Machine Learning and Transfer Metric Learning
Shilpa Choudhary, MD Khaja Shaik, Sivaneasan Bala Krishnan and Sunita Gupta
21.1 Introduction
21.2 Literature Survey
21.3 Proposed System
21.3.1 Using Metric Approximation for TML
21.3.2 Discussion
21.4 Results
21.4.1 Dataset
21.4.2 Evaluation of Performance
21.5 Conclusion
References
22. PacMan-RL: A Game-Changing Approach to Drug Development Through Reinforcement Learning
Abhishek Goud Amkamgari, Harshita Sharma, Rashmi Verma and Bhawna Kaliraman
22.1 Introduction
22.2 Discussion
22.2.1 Reinforcement Learning for Game-Based Drug Design
22.2.2 Neural Networks Used for Biochemical Experimentation
22.2.3 Deep Learning Usage in Drug Development
22.3 Literature Review
22.4 Methodology
22.4.1 Dataset Used
22.4.2 Establishing the State Space
22.4.3 Exploring the Relationship Between Action Space and Decision-Making
22.4.4 Designing Incentives
22.4.5 Education and Fine-Tuning
22.4.6 Evaluation and Measurement of Validity and Efficiency
22.5 Result Analysis
22.6 Model Outcome
22.6.1 Dictionary
22.6.2 Output From the Model
22.6.3 Regenerated SMILES
22.6.4 Molecular Visualization
22.7 Conclusion
References
23. Genetic Variant Classification Through Decision Tree Analysis for Enhanced Genomic Understanding
Prachi Chaudhary and Rajni Mehra
23.1 Introduction
23.2 Literature Survey
23.3 Problem Statement
23.4 Proposed Methodology
23.5 Results and Analysis of Work
23.5.1 Statistical Analysis
23.5.2 Discussion
23.5.3 Findings of Gradient Descent Model
23.5.4 Decision Tree Classification
23.6 Conclusion
References
Index

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