book

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

Name: Genomics at the Nexus of AI, Computer Vision, and Machine Learning
ISBN: 9781394268801

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

October 2024

Intermediate to advanced

560 pages

14h 37m

English

Wiley-Scrivener

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Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Integrating Genomics and Computer Vision: Unravelling Genetic Patterns and Analyzing Genomic Data
1.1 Introduction1.2 Computer Vision in Genomic Research1.3 Image Analysis Techniques for Genomic Data1.4 A Journey Through Computer Vision for Detecting and Analyzing Genetic Patterns1.5 Case Study1.6 Applications of Image Analysis in Genomic Research1.7 Challenges Involved in Analyzing Images for Genomic Data in Computer Vision1.8 ConclusionReferences
2 Syndrome Detection Unleashed: Computer Vision Applications in Neurogenetic Diagnoses
2.1 Introduction2.2 Related Work2.3 Proposed Methodology2.4 Results and Discussion2.5 Conclusion and Future ScopeReferences
3 Integrating Machine Learning for Personalized Kidney Stone Risk Assessment: A Prospective Validation Using CLDN11 Genetic Data and Clinical Factors
3.1 Introduction3.2 Literature Survey3.3 Proposed Methodology3.4 Results and Discussions3.5 Conclusion and Future WorkReferences
4 Unravelling the Complexities of Genetic Codes Through Advanced Machine Learning Algorithms for DNA Sequencing and Analysis
4.1 Introduction4.2 Literature Survey4.3 Proposed Method4.4 Results4.5 ConclusionReferences

5 Deciphering the Complexities of Breast Cancer: Unveiling Resistance Mechanisms
5.1 Introduction5.2 Literature Review5.3 Proposed Methodology5.4 Results5.5 Conclusion and Future ScopeReferences
6 Deciphering the Genetic Terrain: Identifying Genetic Variants in Uncommon Disorders with Pathogenic Effects
6.1 Introduction6.2 Literature Survey6.3 Methodology6.4 Whole Exome Sequencing (WES) with Copy Number Variation (CNV) Analysis6.5 Results and Analysis6.6 ConclusionReferences
7 Genome Data-Based Explainable Recommender Systems: A State-of-the-Art Survey
7.1 Introduction7.2 Literature Survey7.3 Challenges of Explainable Genome Recommendation Systems7.4 Future Directions of Explainable Genome Recommendation Systems7.5 Case Study: Explainable Genome Recommendation Systems for Cancer Treatment7.6 ConclusionReferences
8 Optimizing TCGA Data Analysis: Unveiling Crucial Cancer-Related Gene Alterations Through a Fusion Approach QL Gradient
8.1 Introduction8.2 Literature Survey8.3 Proposed Methodology8.4 Results and Discussion8.5 Conclusion and Future WorkReferences
9 Leveraging Deep Learning for Genomics Analysis: Advances and Applications
9.1 Introduction9.2 Genomics Data Types9.3 State-of-the-Art Deep Learning Models for Genomics Analysis9.4 Importance of Data Preprocessing and Cleaning in Genomics Analysis9.5 Applications of Deep Learning in Genomics Analysis9.6 Challenges in Using Deep Learning in Genomics9.7 Conclusion9.8 Future DirectionsReferences
10 Unraveling Biological Complexity: Leveraging Deep Learning Models for Precise Classification and Understanding of Protein Types and Functions
10.1 Introduction10.2 Literature Work10.3 Proposed Methodology10.4 ResultsReferences
11 The Impact of Learning Techniques on Genomics: Revolutionizing Research and Clinical Breast Cancer Application
11.1 Introduction11.2 Literature Survey11.3 Proposed Methodology11.4 Conclusion11.5 Future ScopeReferences
12 Comparison of Machine Learning and Deep Learning Algorithms for Diabetes Prediction Using DNA Sequences
12.1 Introduction12.2 Literature Survey12.3 Proposed Methodology12.4 Experimental Results12.5 ConclusionReferences
13 AI Applications in Analyzing Gene Expression for Cancer Diagnosis: A Comprehensive Review
13.1 Introduction13.2 Expression of Gene Data13.3 Feature Selection Methods for Gene Expression Analysis13.4 ML/DL Methods for Gene Expression Analysis13.5 Graph Analysis13.6 ConclusionReferences
14 Optimum Detection of Human Genome Related to Cancer Cells Using Signal Processing
14.1 Introduction14.2 Methodology14.3 Results and Discussion14.4 ConclusionReferences
15 Genomics-Driven Strategies for Sustainable Crop Improvement in Agriculture
15.1 Introduction15.2 Related Work15.3 Problem Statement15.4 Proposed Model15.5 Results and Discussion15.6 Conclusion and Future ScopeReferences
16 An Efficient Deep Convolutional Neural Networks Model for Genomic Sequence Classification
16.1 Introduction16.2 Case Study16.3 Results16.4 Limitations of Deep Learning in Genomics16.5 Conclusion and Future DirectionsReferences
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
17.1 Introduction17.2 Literature Review17.3 Analysis of SLC26A1 Gene for Kidney Stone Prediction17.4 Functions of SLC26A117.5 Categories of Confidence17.6 ConclusionReferences
18 A Comprehensive Approach for Enhancing Kidney Disease Detection Using Random Forest and Gradient Boosting
18.1 Introduction18.2 Literature Survey18.3 Problem Statement18.4 Proposed Methodology18.5 Experimental Results and Analysis18.6 ConclusionReferences
19 Decoding the Future: COVID-19 RNA Sequence Prediction Through LSTM Transformation
19.1 Introduction19.2 Literature Survey19.3 Proposed System19.4 Experimental Setup and Discussion19.5 Conclusion and Future ScopeReferences
20 Genomics and Machine Learning: ML Approaches, Future Directions and Challenges in Genomics
20.1 Introduction20.2 Unique Characteristics of Genomics Data20.3 Significance of Genomics Data in AI and ML20.4 ML Approaches Applied in Genomics Research and Their Applications20.5 Contributions to ML Approaches in Genomic Data Analysis20.6 Gene Expression Prediction and Disease Classification Using ML20.7 Challenges in Genomics20.8 Future Directions in GenomicsReferences
21 Predicting Gene Ontology Annotations from CAFA Using Distance Machine Learning and Transfer Metric Learning
21.1 Introduction21.2 Literature Survey21.3 Proposed System21.4 Results21.5 ConclusionReferences
22 PacMan-RL: A Game-Changing Approach to Drug Development Through Reinforcement Learning
22.1 Introduction22.2 Discussion22.3 Literature Review22.4 Methodology22.5 Result Analysis22.6 Model Outcome22.7 ConclusionReferences
23 Genetic Variant Classification Through Decision Tree Analysis for Enhanced Genomic Understanding
23.1 Introduction23.2 Literature Survey23.3 Problem Statement23.4 Proposed Methodology23.5 Results and Analysis of Work23.6 ConclusionReferences
Index
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Content preview from Genomics at the Nexus of AI, Computer Vision, and Machine Learning

6Deciphering the Genetic Terrain: Identifying Genetic Variants in Uncommon Disorders with Pathogenic Effects

Nikhila Kathirisetty¹^*, Ravula Arun Kumar², G. Suryanarayana³, Farhana Begum¹, C. Padmini⁴ and Pravin Tirgar⁵

¹Dept of CSD, Vardhaman College of Engineering, Hyderabad, India

²Dept of CSE, Vardhaman College of Engineering, Hyderabad, India

³Dept of IT, Vardhaman College of Engineering, Hyderabad, India

⁴Dept of ECE, Vardhaman College of Engineering, Hyderabad, India

⁵School of Pharmacy, RK University, Rajkot, Gujarat, India

Abstract

Rare genetic diseases are difficult to diagnose and treat due to their complexity and individual variability. Identifying genetic variants to determine the causes of these diseases is crucial. This chapter concentrates on identifying disease-causing variants in rare genetic disorders and analyzing how these variants influence the onset and spread of disease. The genetic mutations responsible for these diseases are identified using advanced sequencing techniques such as whole exome (WE), genome, and targeted gene panel (TGP) sequencing. Functional characterization investigations clarify the molecular processes and biological pathways affected by the discovered variations using in vitro and in vivo models. Also discussed is how discovering genetic variants could improve diagnosis, lead to more individualized treatment plans, and enhance patient outcomes. This chapter discusses the significance of finding genetic variants to learn about rare ...

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

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