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

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 736

Veröffentlichungsjahr: 2024

Beschreibung

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.

Leseprobe

Table of Contents

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 Introduction

1.2 Computer Vision in Genomic Research

1.3 Image Analysis Techniques for Genomic Data

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

2.1 Introduction

2.2 Related Work

2.3 Proposed Methodology

2.4 Results and Discussion

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

3.1 Introduction

3.2 Literature Survey

3.3 Proposed Methodology

3.4 Results and Discussions

3.5 Conclusion and Future Work

References

4 Unravelling the Complexities of Genetic Codes Through Advanced Machine Learning Algorithms for DNA Sequencing and Analysis

4.1 Introduction

4.2 Literature Survey

4.3 Proposed Method

4.4 Results

4.5 Conclusion

References

5 Deciphering the Complexities of Breast Cancer: Unveiling Resistance Mechanisms

5.1 Introduction

5.2 Literature Review

5.3 Proposed Methodology

5.4 Results

5.5 Conclusion and Future Scope

References

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

6.1 Introduction

6.2 Literature Survey

6.3 Methodology

6.4 Whole Exome Sequencing (WES) with Copy Number Variation (CNV) Analysis

6.5 Results and Analysis

6.6 Conclusion

References

7 Genome Data-Based Explainable Recommender Systems: A State-of-the-Art Survey

7.1 Introduction

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

8.1 Introduction

8.2 Literature Survey

8.3 Proposed Methodology

8.4 Results and Discussion

8.5 Conclusion and Future Work

References

9 Leveraging Deep Learning for Genomics Analysis: Advances and Applications

9.1 Introduction

9.2 Genomics Data Types

9.3 State-of-the-Art Deep Learning Models for Genomics Analysis

9.4 Importance of Data Preprocessing and Cleaning in Genomics Analysis

9.5 Applications of Deep Learning in Genomics Analysis

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

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

11.1 Introduction

11.2 Literature Survey

11.3 Proposed Methodology

11.4 Conclusion

11.5 Future Scope

References

12 Comparison of Machine Learning and Deep Learning Algorithms for Diabetes Prediction Using DNA Sequences

12.1 Introduction

12.2 Literature Survey

12.3 Proposed Methodology

12.4 Experimental Results

12.5 Conclusion

References

13 AI Applications in Analyzing Gene Expression for Cancer Diagnosis: A Comprehensive Review

13.1 Introduction

13.2 Expression of Gene Data

13.3 Feature Selection Methods for Gene Expression Analysis

13.4 ML/DL Methods for Gene Expression Analysis

13.5 Graph Analysis

13.6 Conclusion

References

14 Optimum Detection of Human Genome Related to Cancer Cells Using Signal Processing

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

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

16.1 Introduction

16.2 Case Study

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

17.1 Introduction

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

18.1 Introduction

18.2 Literature Survey

18.3 Problem Statement

18.4 Proposed Methodology

18.5 Experimental Results and Analysis

18.6 Conclusion

References

19 Decoding the Future: COVID-19 RNA Sequence Prediction Through LSTM Transformation

19.1 Introduction

19.2 Literature Survey

19.3 Proposed System

19.4 Experimental Setup and Discussion

19.5 Conclusion and Future Scope

References

20 Genomics and Machine Learning: ML Approaches, Future Directions and Challenges in Genomics

20.1 Introduction

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

21.1 Introduction

21.2 Literature Survey

21.3 Proposed System

21.4 Results

21.5 Conclusion

References

22 PacMan-RL: A Game-Changing Approach to Drug Development Through Reinforcement Learning

22.1 Introduction

22.2 Discussion

22.3 Literature Review

22.4 Methodology

22.5 Result Analysis

22.6 Model Outcome

22.7 Conclusion

References

23 Genetic Variant Classification Through Decision Tree Analysis for Enhanced Genomic Understanding

23.1 Introduction

23.2 Literature Survey

23.3 Problem Statement

23.4 Proposed Methodology

23.5 Results and Analysis of Work

23.6 Conclusion

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Comparative analysis of different algorithms.

Chapter 3

Table 3.1 Model comparison.

Chapter 4

Table 4.1 The RNN evaluation outcome.

Table 4.2 Evaluation parameters from confusion matrix.

Table 4.3 Training and validation accuracy curves for state-of-the-art techniq...

Chapter 5

Table 5.1 Comparison between the models used.

Chapter 6

Table 6.1 Expansion of next-generation sequencing.

Chapter 7

Table 7.1 List of explainability methods.

Table 7.2 Literature for explainable genome recommendation systems.

Table 7.3 Challenges and their potential solutions.

Chapter 8

Table 8.1 Difference between quantum and machine learning approaches.

Table 8.2 Analysis of the existing approaches.

Table 8.3 Genetic datasets description.

Table 8.4 Correlation between rs_KRT23 and rs_APOB.

Table 8.5 Interaction between TPR and FPR.

Chapter 9

Table 9.1 Genomic-level deep-learning applications.

Table 9.2 Deep-learning applications for transcriptomic level.

Table 9.3 Examples of personalized medicine.

Table 9.4 Case studies of cancer genomics.

Chapter 10

Table 10.1 Existing literature survey on protein classifications.

Table 10.2 Label distribution of data.

Table 10.3 Data analysis of dataset.

Table 10.4 Model parameters for training RNN+LSTM.

Table 10.5 Training history of RNN+LSTM model.

Chapter 11

Table 11.1 Listing of all the classifiers with different performance metrics.

Chapter 12

Table 12.1 Performance analysis of the proposed system for diabetes prediction...

Chapter 13

Table 13.1 Comparison of microarray with RNA-Seq data.

Chapter 14

Table 14.1 Twenty amino acid and codon lists.

Table 14.2 Genes associated with cancer and non-cancer cells in humans.

Chapter 15

Table 15.1 Comparative summary of key findings from previous studies in genomi...

Table 15.2 Parameters for precision breeding research in wheat.

Table 15.3 Comparison table with previous works.

Table 15.4 CRISPR-Cas9: qualitative insights into organismal trait.

Table 15.5 Quantitative analysis of editing efficiency, off-target effects, an...

Chapter 16

Table 16.1 Performance parameters of hyperparameter fine-tuned CNN model.

Chapter 17

Table 17.1 Study on existing methodologies.

Chapter 18

Table 18.1 Comprehensive survey of machine learning approaches for kidney dise...

Table 18.2 Dataset with features and records.

Table 18.3 Comprehensive finding of machine learning approaches for kidney dis...

Table 18.4 Shows minimal computational burden.

Table 18.5 Comprehensive comparison of proposed and previous methods.

Chapter 19

Table 19.1 Training data of LSTM model.

Table 19.2 Layer description of transformer model.

Chapter 21

Table 21.1 Probability of prediction at the end of TML.

Table 21.2 Correlation matrix.

Chapter 23

Table 23.1 Comprehensive overview of genetic variant classification studies us...

List of Illustrations

Chapter 1

Figure 1.1 Year-by-year progress in human genomics projects [1].

Figure 1.2 Genomics sequence [6].

Figure 1.3 Genome mining is associated with bioinformatics investigations [10]...

Figure 1.4 Computer vision works process [11].

Figure 1.5 Gel picture for fungal extracted DNA (18S gene amplified fraction).

Figure 1.6 Phylogenetic tree.

Chapter 2

Figure 2.1 Positional or deformational plagiocephaly and lambdoid synostosis.

Figure 2.2 Types of synostosis.

Figure 2.3 The syndrome looks.

Figure 2.4 Proposed architecture.

Figure 2.5 Image dataset with label and features of each syndrome.

Figure 2.6 Loading the model YOLOv5s.

Figure 2.7 Training for 20 epochs.

Figure 2.8 Label correlogram and labels.

Figure 2.9 Recall-confidence curve.

Figure 2.10 Precision-recall curve.

Figure 2.11 Precision-confidence curve.

Figure 2.12 Training losses, various metrics, validation losses, and learning ...

Chapter 3

Figure 3.1 Various genes which affect kidney stone formation.

Figure 3.2 Proposed methodology.

Figure 3.3 SVM model implementation.

Figure 3.4 SVM model implementation with error bars.

Figure 3.5 SVM model implementation with error bar details.

Figure 3.6 Logistic regression implementation graph.

Figure 3.7 Logistic regression model implementation with error bar details.

Figure 3.8 Random forest implementation graph.

Figure 3.9 Comparative analysis of various models.

Chapter 4

Figure 4.1 DNA sequence [1].

Figure 4.2 An overview of the deep learning model (DLM) and targeted NGS panel...

Figure 4.3 Dataset of DNA sequence of humans.

Figure 4.4 Distribution of DNA sequencing.

Figure 4.5 Feed forward neural network.

Figure 4.6 Recurrent neural network with GRU architecture [20].

Figure 4.7 Neural network activation function.

Figure 4.8 Slide movement for individual stride.

Figure 4.9 (a, b, c). Class distributions of each subject of data.

Figure 4.10 (a, b, c). Confusion matrix for chimpanzee, dog, and human (from l...

Figure 4.11 Graphical representation of evaluation parameters.

Chapter 5

Figure 5.1 Workflow of proposed work.

Figure 5.2 Truth table.

Figure 5.3 K-means clustering.

Figure 5.4 Confusion matrix of KNN (K-nearest neighbors).

Figure 5.5 ROC curve of KNN (K-nearest neighbors).

Figure 5.6 Confusion matrix of DT (decision tree).

Figure 5.7 ROC curve of DT (decision tree).

Figure 5.8 Confusion matrix of RF (random forest).

Figure 5.9 ROC curve of RF (random forest).

Figure 5.10 Confusion matrix of support vector machine.

Figure 5.11 ROC curve of support vector machine.

Chapter 6

Figure 6.1 Flow graph for genome sequence to find rare ailments.

Figure 6.2 Cohort for distribution of variants.

Figure 6.3 Age distribution of cohorts.

Figure 6.4 Distribution of severe cases.

Figure 6.5 Gender distribution of cohorts.

Figure 6.6 Disease progression vs age.

Figure 6.7 Commonly affected genes.

Chapter 7

Figure 7.1 Benefits of personalized medicine.

Figure 7.2 Architecture of a personalized recommendation system.

Figure 7.3 Classification of XAI methods.

Figure 7.4 Basic genome recommendation system.

Figure 7.5 Explainable genome recommendation system.

Chapter 8

Figure 8.1 Traditional ML approach.

Figure 8.2 Ensemble approach.

Figure 8.3 Selection of optimal values based on the gradient boosting.

Figure 8.4 Quantum optimization ML.

Figure 8.5 Outlier analyses on the survival rate.

Figure 8.6 Training and testing data confusion matrix.

Figure 8.7 Scatter analysis.

Figure 8.8 ROC evaluation for proposed model.

Chapter 9

Figure 9.1 Genomic data types.

Figure 9.2 Deep learning applications in genomics (adapted from reference [7])...

Figure 9.3 Evolution of DNA sequencing (adapted from Satam

et al

., 2023 [16]).

Figure 9.4 Different omics levels in genomics adapted from reference [22].

Figure 9.5 Personalized medicine workflow, adapted from reference [35].

Figure 9.6 Categorization of drug discovery problems, adapted from reference [...

Chapter 10

Figure 10.1 Codon sequences that make up each amino acid.

Figure 10.2 (a) Proposed architecture. (b) VAE calculation using Kullback-Leib...

Figure 10.3 Confusion matrix of label prediction using LSTM.

Figure 10.4 Accuracy loss graphs of LSTM architecture.

Figure 10.5 Plot using crystallization method on each protein class.

Figure 10.6 Box plot of resolution of protein class.

Figure 10.7 Box plot of molecular weight of protein class.

Figure 10.8 Box plot of temperature of protein class.

Figure 10.9 Pair plot of parameters.

Chapter 11

Figure 11.1 Nucleotide sequence.

Figure 11.2 Reverse complement.

Figure 11.3 Survival and recurrence rate w.r.t. age_at_diagnosis.

Figure 11.4 Survival and recurrence rate w.r.t. death_from_cancer.

Figure 11.5 Tumor size and overall survival.

Figure 11.6 Venn diagram of patients with different levels of treatment.

Figure 11.7 Distribution of histopathological class and survival.

Figure 11.8 Histogram correlation of genes with the survival.

Figure 11.9 Accuracy score and ROC curve.

Chapter 12

Figure 12.1 DNA structure [6].

Figure 12.2 Classification of DM by the World Health Organization (WHO).

Figure 12.3 The process of transforming DNA into proteins.

Figure 12.4 Mutated sequence vs normal human insulin gene sequence [26].

Figure 12.5 Proposed system model.

Figure 12.6 Accuracy, recall, and precision analysis of the proposed system fo...

Chapter 13

Figure 13.1 Genome sequence analysis.

Figure 13.2 Classification of different feature engineering methods: (a) filte...

Figure 13.3 Detection of breast cancer.

Figure 13.4 Basic ANN structure [17].

Figure 13.5 Pairwise plots of cancer dataset attributes using ML and DL [28].

Chapter 14

Figure 14.1 Flow diagram of the proposed methodology.

Figure 14.2 An illustration of the codon to amino acid mapping.

Figure 14.3 PSD bar plots of cancer cells.

Figure 14.4 PSD bar plots of non-cancer cells.

Figure 14.5 Comparing cancer and non-cancer cells’ average PSD values.

Chapter 15

Figure 15.1 Sequential steps involved in the precision breeding methodology.

Figure 15.2 Comparison of editing efficiency.

Figure 15.3 Comparison of off-target effects.

Figure 15.4 Comparison of stability of edit.

Chapter 16

Figure 16.1 Structure of convolutional neural network.

Figure 16.2 The architecture of the CNN system.

Figure 16.3 Model loss in terms of training and validation.

Figure 16.4 Model accuracy in terms of training and validation.

Figure 16.5 Confusion matrix for the genome analysis.

Figure 16.6 The architecture of the hyperparameter fine-tuned CNN system.

Figure 16.7 Model loss in terms of training and validation of hyperparameter f...

Figure 16.8 Model accuracy in terms of training and validation of hyperparamet...

Figure 16.9 Confusion matrix for the genome analysis.

Chapter 17

Figure 17.1 Types of kidney diseases [5].

Figure 17.2 Subcellular localization of SLC26A1 and its impact on kidney and i...

Figure 17.3 Structure of SLC26A1 (source: SLC26A1 - Sulfate anion transporter ...

Chapter 18

Figure 18.1 Proposed methodology for CKD.

Figure 18.2 Comparison of different machine learning approaches for kidney dis...

Figure 18.3 Accuracy plot: proposed vs. other.

Figure 18.4 Precision plot: proposed vs. other.

Figure 18.5 F1-score plot: proposed vs. other.

Figure 18.6 Recall plot: proposed vs. other.

Figure 18.7 Overall comparison.

Chapter 19

Figure 19.1 Structure of DNA [1].

Figure 19.2 RNA overview [1].

Figure 19.3 Proposed LSTM model (contains sigmoid activations, pairwise multip...

Figure 19.4 Worldwide cases analysis.

Figure 19.5 Rate of cases confirmed country wise.

Figure 19.6 LSTM model.

Figure 19.7 Model evaluation of LSTM.

Figure 19.8 Mutation % calculated for future subject.

Figure 19.9 Multi-head attention layers running in parallel.

Figure 19.10 Plots of accuracy using transformer model.

Figure 19.11 Prediction of mutation rate for upcoming patient.

Chapter 20

Figure 20.1 DNA sequencing [1].

Figure 20.2 RNA sequencing [2].

Chapter 21

Figure 21.1 Gene ontology (GO) lineage relations [16].

Figure 21.2 Proposed methodology.

Figure 21.3 Description of dataset.

Figure 21.4 After target load.

Figure 21.5 Data occurrence of BPO, CCO and MFO.

Figure 21.6 F1-score accuracy prediction of biological process.

Figure 21.7 AUC and F1 averaged.

Figure 21.8 Modelling curves.

Figure 21.9 Display of top 20 data after blending.

Figure 21.10 Distortion score using K-means clustering.

Figure 21.11 Protein distribution in clusters for K-means.

Figure 21.12 Silhouette coefficient cluster.

Figure 21.13 Cluster-wise similarity matrices.

Figure 21.14 ROC AUC scores for each label for multilabel prediction.

Chapter 22

Figure 22.1 Chemical latent space.

Figure 22.2 Conditions of control compound.

Figure 22.3 Generation at once or sequentially.

Figure 22.4 Proposed methodology.

Figure 22.5 Dataset used.

Figure 22.6 2D representations of molecules and color-code.

Figure 22.7 Pandas series with the counts of each atom type.

Figure 22.8 Molecular representation.

Figure 22.9 Graph for molecule representation.

Figure 22.10 Molecular weight.

Figure 22.11 Generated structure.

Chapter 23

Figure 23.1 Proposed methodology for genetic variant classification.

Figure 23.2 Class distribution of the dataset.

Figure 23.3 Stacked bar graph with the top 50 genes.

Figure 23.4 Heatmap of mask and aspect ratio.

Figure 23.5 Histogram of the data values.

Figure 23.6 Features of ClinVar (‘CLNVC’, ‘IMPACT’, ‘SIFT’, ‘PolyPhen’ ).

Figure 23.7 Receiver operating characteristic.

Figure 23.8 ROC of decision tree classifier.

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

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

Edited by

Shilpa Choudhary

CSE (AIML), Neil Gogte Institute of Technology, Hyderabad, India

Sandeep Kumar

Department of CSE-H, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India

Swathi Gowroju

Sreyas Institute of Engineering & Technology, Hyderabad, India

Monali Gulhane

Symbiosis Institute of Technology, Pune, India

and

R. Sri Lakshmi

Singapore Institute of Technology, Singapore

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-26880-1

Cover image: Pixabay.ComCover design by Russell Richardson

Preface

This book is a comprehensive guide that will help readers understand the dynamic intersection between genomics and cutting-edge technologies. In an era where scientific progress accelerates unprecedentedly, this volume aims to provide a roadmap for navigating the intricate landscape where genomics converges with artificial intelligence (AI), computer vision, and machine learning. These developments have opened up new research, innovation, and exploration avenues.

The rapid advancements in genomics have propelled our understanding of the intricacies of life itself. Genomics is a cornerstone of biological research, from unravelling the genetic basis of diseases to unlocking the secrets of evolution. Concurrently, AI, computer vision, and machine learning have witnessed extraordinary strides, transformed industries, and reshaped how we process information. The convergence of these fields offers unparalleled opportunities for innovation, promising breakthroughs that could revolutionize personalized medicine, drug discovery, and beyond.

On this intellectual journey, our primary objective is to demystify the synergies between genomics and the triad of AI, computer vision, and machine learning. This book has been crafted for a diverse audience, spanning researchers, clinicians, students, and industry professionals, and fosters a shared understanding of the transformative potential within our grasp.

Each chapter unfolds a unique facet of the interdisciplinary landscape, delving into the collaborative potential and challenges that arise at the nexus of genomics and advanced technologies. From deciphering the genomic code through state-of-the-art algorithms to leveraging computer vision for insightful data analysis, the book explores the methodologies shaping the future of genomic research.

We owe our gratitude to the contributors who have dedicated their expertise to this endeavour, offering insights that span the spectrum from theoretical frameworks to practical applications. We invite you to embark on a journey that transcends traditional boundaries, exploring the frontiers of knowledge where genomics, AI, computer vision, and machine learning converge. Together, we navigate this intricate nexus, seeking to decode the mysteries of life through the lens of interdisciplinary collaboration. We also extend our thanks to the reviewers who have provided valuable feedback to the authors and helped improve the quality of the articles. The editors also thank Scrivener Publishing and their team members for the opportunity to publish this volume. Lastly, we thank our family members for their love, support, encouragement, and patience during this work.

We hope this book will be valuable for researchers, professionals, and students interested in Genomics, and inspire further research and innovation that contributes to the development of new applications and technologies. We look forward to future advancements in Genomics and hope this book will play a small role in shaping the future of this exciting field.

Shilpa Choudhary

Sandeep Kumar

G. Swathi

Monali Gulhane

R. Sri Lakshmi

1Integrating Genomics and Computer Vision: Unravelling Genetic Patterns and Analyzing Genomic Data

Neha Tanwar1, Sandeep Kumar2*, Garima Singh3 and Monika Bhakta4

1Department of Food Technology, Guru Jambheshwar University of Science and Technology, Hisar, India

2Engineering Cluster, Singapore Institute of Technology (SIT), 10 Dover Drive, Singapore, Singapore

3Department of Law, Bennett University, Greater Noida, India

4Department of Law, Sangam University, Bhilwara, Rajasthan, India

Abstract

In recent years, genomics and computer vision have undergone significant advancements that have profoundly influenced scientific research and healthcare. Genomics, which involves studying an organism’s complete DNA sequence, is crucial in understanding the genetic basis of diseases and designing personalized treatment strategies. Conversely, computer vision, a subfield of artificial intelligence, concentrates on creating algorithms and methodologies for analyzing and interpreting visual data. This chapter offers an overview of the convergence of genomics and computer vision, emphasizing the application of image analysis techniques for genomic data and the detection and analysis of genetic patterns using computer vision methods. The rapid progress in high-throughput sequencing technologies has led to a remarkable increase in the volume of genomic data generated. This abundance of genetic information necessitates efficient and accurate analysis methods, wherein computer vision techniques are indispensable.

A prominent area of research in integrating genomics and computer vision is using image analysis techniques for genomic data. The analysis and interpretation of complex genomic data require the development of sophisticated algorithms capable of identifying various types of genetic patterns. With their capability to extract meaningful features from visual data, computer vision methods have demonstrated their value in analyzing genomic sequences and identifying genetic variations. This interdisciplinary approach holds great promise for advancing genomic research and enhancing healthcare applications. The combination of genomics and computer vision has diverse applications, including detecting and analyzing genetic patterns. Computer vision algorithms can effectively uncover spatial or temporal relationships in genetic data, such as mutations or gene expression levels. This integration has revolutionized scientific research and healthcare, enabling more profound insights into disease biology. The collaboration between genomics and computer vision will drive future discoveries and innovations as genomics advances and generates vast amounts of data.

Keywords: Genomics, computer vision, machine learning, genetics, genome sequencing

1.1 Introduction

Computer vision is a specialized area within artificial intelligence which concentrates on the scientific and technological aspects of enabling machines to perceive and interpret the physical world through visual data. Computer vision is an interdisciplinary field that focuses on allowing computers to analyze and understand visual information from the world around us. Its primary goal is to empower computers with the capability to extract, research, and comprehend information from images or video sources.

Computer vision applications span multiple fields, including medicine, robotics, surveillance, and, more recently, genomic research. It uses digital images and videos as input data to replicate human vision capabilities, such as object recognition, scene understanding, and image analysis. Computer vision is crucial in various applications, including autonomous vehicles, facial recognition, medical imaging, surveillance systems, and robotics [2]. This technology has witnessed significant advancements in recent years, primarily driven by advances in deep learning and neural network architectures.

In the context of genomics, computer vision can be used to analyze and interpret genomic data, which includes DNA sequences, gene expression profiles, and genomic images obtained through advanced imaging techniques [3]. This field of research, known as genomic vision, can enhance our understanding of genomics and contribute to various aspects of biological and medical research, as shown in Figure 1.1.

Figure 1.1 Year-by-year progress in human genomics projects [1].

The roots of computational genomics are intertwined with those of bioinformatics. In the 1960s, Margaret Dayhoff and her colleagues at the National Biomedical Research Foundation compiled databases of homologous protein sequences to study evolution. They created a phylogenetic tree based on amino acid sequences to understand the changes required for one protein to transform into another. This led to developing a scoring matrix that assessed the likelihood of protein-relatedness [4]. Genomics, often called functional genomics, has a broad scope aiming to understand the functions of all genomic elements in an organism. This involves using genome-scale assays like genome sequencing, transcriptome profiling, and proteomics. Unlike hypothesis-driven approaches, genomics relies on data exploration to discover novel properties and associations from large-scale genomic data.

Due to the vast and complex nature of genomics data, more than a visual examination of pairwise correlations is required. Analytical tools, especially machine learning algorithms, are essential to uncover unexpected relationships, generate new hypotheses, and make predictions. Machine learning algorithms are well-suited for data-driven sciences, including genomics, as they automatically detect patterns in the data without relying on hard-coded assumptions or domain expertise. However, the effectiveness of machine learning algorithms heavily depends on how the data is represented, i.e., how the features are computed. The quality and relevance of these features significantly impact the performance of classification tasks. For example, in tumor classification from fluorescent microscopy images, handcrafted elements such as cell counts might not fully capture relevant visual characteristics like cell morphology, cell distances, or organ localization, leading to reduced classification accuracy. Thus, improving feature representation is a central concern in genomics research.

In the 1980s, genome sequence databases emerged, posing new challenges for searching and comparing gene information. Unlike simple text-searching algorithms used for regular websites, genetic similarity requires finding similar rather than identical strings. The Needleman-Wunsch algorithm was developed, utilizing scoring matrices from Dayhoff’s research to compare amino acid sequences [5]. Later, the BLAST algorithm was introduced for fast, optimized searches of gene sequence databases and remains widely used today. The term “computational genomics” gained popularity in the mid-to-late 1990s when complete sequenced genomes became available. The Annual Conference on Computational Genomics, initiated by scientists from The Institute for Genomic Research (TIGR) in 1998, distinguished this speciality from broader fields like genomics and computational biology [5]. Its first use in scientific literature was in nucleic acids research in the preceding year. Key conferences include Intelligent Systems for Molecular Biology (ISMB) and Research in Computational Molecular Biology (RECOMB).

The precise arrangement of nucleotides—the building blocks of DNA— in the genome of a given organism is referred to as its genomic sequence (as shown in Figure 1.2). The genome is an organism’s whole collection of genetic material, or DNA in most cases, which contains the instructions needed to develop, maintain, and operate that particular creature. Within the field of genomics, which focuses on the examination of complete genomes, a genomic sequence offers a guide for comprehending the genetic data contained in an organism’s DNA [7].

DNA is composed of four central nucleotides: adenine (A), thymine (T), cytosine (C), and guanine (G). A and C couple with T and G, respectively, in a complementary way to make pairs. The genomic sequence is the linear configuration of these nucleotides along the DNA strand [8]. Essential facts regarding chromosomal sequencing:

Base pairs:

typically, genomic sequences are shown as letters for nucleotides. A brief genetic sequence, for instance, might be represented as “ATCGGA.”

Genes and non-coding areas:

among other things, non-coding areas in genomic sequences perform regulatory roles. Coding regions, on the other hand, contain instructions for making proteins or genes.

Variability:

individuals within the same species can have dramatically different genomic sequences. Understanding genetic diversity, inheritance patterns, and disease risk is aided by studying these variants.

Genomic information:

the information needed for an organism’s growth, development, and operation is included in its genome sequence. Understanding the genetic underpinnings of different traits and disorders and finding genes and regulatory elements depend on deciphering these sequences.

Technologies for mapping and sequencing genomes

: genomics has been revolutionized thanks to sequencing and genome mapping advances, including next-generation sequencing (NGS). These technologies make large-scale genomic research possible, enabling quick and affordable determination of genomic sequences.

Comparative genomics:

to determine evolutionary links and similarities and differences between various species or individuals, comparative genomics compares their genomic sequences. This method sheds light on the evolutionary similarities and differences between organisms.

Figure 1.2 Genomics sequence [6].

Numerous facets of genomics research, such as functional genomics, genome annotation, and customized medicine, are based on genetic sequences. Deciphering an organism’s genomic sequence is essential to understanding genetic information, gene expression, and the complex systems that underlie life activities.

Using tools like Mathematica or Matlab, computer-assisted mathematics facilitated engineers, mathematicians, and computer scientists’ engagement in this domain. A growing collection of case studies and demonstrations spans from whole genome comparisons to gene expression analysis. This integration of diverse ideas includes concepts from systems and control, information theory, string analysis, and data mining. Computational approaches are becoming standard in research and teaching, leading to the development of students well-versed in both genomics and computational techniques. As shown in Figure 1.3, genomic research is an ever-progressing field that investigates genome structure, function, and evolution. A genome encompasses an organism’s genetic material, whether DNA or RNA. Advancements in genomics are instrumental in comprehending gene mechanisms, pinpointing disease-causing variations, and foreseeing personalized responses to treatments [9]. Nonetheless, a significant obstacle faced in genomic research is the substantial volume of data generated through sequencing technologies, necessitating sophisticated computational techniques for analysis and interpretation. Here is where computer vision emerges as a crucial tool to aid researchers.

Figure 1.3 Genome mining is associated with bioinformatics investigations [10].

Computer vision techniques, which have proven their efficacy in various domains, can now be employed in genomics to efficiently process and analyze the vast amount of genomic data. By utilizing pattern recognition, machine learning algorithms, and deep learning models, computer vision can assist in identifying genetic variations, predicting gene functions, and uncovering meaningful insights from complex genomic datasets.

The integration of groundbreaking computer vision with genomic research has the potential to accelerate scientific discoveries and facilitate the development of personalized medicine. However, it is vital to ensure the ethical use of data and maintain stringent privacy measures when dealing with sensitive genetic information. Embracing this interdisciplinary approach, researchers can harness the power of computer vision to unlock the secrets encoded within the genomes, leading to groundbreaking advancements in understanding and treating various diseases. Here are some examples of how computer vision is being used in genomics:

DNA sequence analysis:

computer vision techniques can analyze DNA sequences and identify patterns associated with a disease or other traits. For example, researchers have used computer vision to identify mutations in DNA sequences associated with cancer.

Gene expression analysis:

computer vision can analyze gene expression profiles, providing information about how genes are expressed in different cells and tissues. For example, researchers have used computer vision to identify genes abnormally expressed in cancer cells.

Genomic imaging:

computer vision can be used to analyze genomic images, which can provide information about the structure and organization of DNA. For example, researchers have used computer vision to identify DNA damage in cells that are exposed to radiation.

Genomic vision is a rapidly developing field with the potential to revolutionize our understanding of genomics. We expect to see even more innovative computer vision applications in genomics research as technology advances.

In the context of genomic research, integrating computer vision algorithms and techniques has emerged as a valuable tool. The enormous amount of genomic data generated through sequencing technologies poses significant challenges in analysis and interpretation. By leveraging computer vision, researchers can efficiently process and analyze this vast amount of data, leading to quicker and more accurate identification of genetic variations, gene functions, and other relevant insights. The potential benefits of applying computer vision in genomic research are far-reaching. It can accelerate scientific discoveries, aid in developing personalized medicine, and contribute to a better understanding of the complex relationships between genes and diseases. However, it is crucial to approach this interdisciplinary approach responsibly, adhering to ethical guidelines and ensuring the privacy and security of sensitive genetic information.

Figure 1.4 Computer vision works process [11].

Computer vision is a field of computer science that deals with extracting meaningful information from digital images or videos. It is a highly interdisciplinary field, drawing on techniques from artificial intelligence, machine learning, image processing, and statistics. The key processes involved in computer vision can be summarized as follows, shown in Figure 1.4.

1.2 Computer Vision in Genomic Research

Computer vision techniques have been increasingly adopted in genomic research due to their ability to analyze and interpret complex genomic datasets. Here are some areas where computer vision is involved in genomic analysis [12]:

Image analysis:

it is standard procedure in genomics to visualize and analyze microscopic structures, such as chromosomes, cells, and tissues. Computer vision algorithms are critical in autonomously identifying and quantifying particular genetic traits from these images. This helps to produce more precise and efficient studies by doing activities like counting chromosomal abnormalities or identifying the existence of genetic alterations.

Assembly of the genome:

short segments of DNA or RNA produced by genome sequencing must be assembled into a whole genome. This procedure is greatly facilitated by computer vision algorithms, which align, map, and make these fragments. Computer vision simplifies the complex process of genome assembly in genomic research by identifying overlapping regions that aid in reconstructing whole genomes.

Variant detection:

in genomic research, it is essential to identify genetic variants, such as single nucleotide polymorphisms (SNPs) and structural changes. Computer vision methods are necessary for variation detection because they enable comparing and aligning massive genomic datasets with reference genomes. This method improves variant calling’s precision and effectiveness while giving scientists a thorough understanding of genomic variants.

Gene expression study:

to understand gene regulation and its significance in diseases, genomic research frequently explores the study of gene expression patterns. Computer vision is functional when analyzing pictures and data from methods like in situ hybridization or immunohistochemistry [

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]. Computer vision helps us comprehend genes and their roles in biological processes by measuring gene expression levels and spatial patterns. Applying computer vision to genomics research opens up new avenues for efficiency and precision while also speeding up data analysis.

Computer vision techniques are expected to become more and more critical in deciphering the complexities of genetic information as genomic databases continue to grow in size and complexity. This will ultimately advance our understanding of genomics and its implications for health and illness.

1.3 Image Analysis Techniques for Genomic Data

Genomic research generates vast amounts of data in the form of images that require sophisticated analysis and interpretation techniques. These images range from microscopic structures like chromosomes and cells to large, high-resolution images of entire organs and tissues. Many factors must be considered regarding genomic photos, such as noise reduction, feature detection, classification, and segmentation. This article will discuss some image analysis techniques used in genomic research and their applications [15, 19].

1.3.1 Preprocessing Techniques

The first step in analyzing genomic images is to preprocess them to remove noise, correct for artefacts, and enhance the features of interest. Preprocessing techniques eliminate unwanted noise and adjust the image’s contrast and brightness to discern the elements of interest more easily.

Image normalization:

its use in genomics: image normalization is essential to guarantee uniformity in pixel values among images. Pixel intensities in genomic imaging can differ due to differences in imaging settings like lighting or staining. By bringing pixel values into a uniform range, normalization makes it easier to compare disparate images fairly and accurately. Image normalization aids in attaining consistency in intensity, enabling accurate assessment of fluorescence signals linked to particular genetic markers in applications such as chromatin imaging and fluorescence microscopy.

Filtering techniques:

in genomics, filtering techniques improve image quality by highlighting particular characteristics or lowering noise. Sharpening filters improve edge identification for a more distinct separation of genetic structures, whereas smoothing filters—like Gaussian filters—eliminate high-frequency noise from genomic images. Filtering can increase the signal-to-noise ratio, which helps with correct segmentation and analysis of genetic patterns in DNA microscopy and cytogenetic imaging, where exact identification of genetic structures is essential.

Contrast enhancement:

the visual quality of genomic images is enhanced by applying contrast enhancement techniques, which facilitate the identification of minute details. Meanwhile, nonlinear adjustments, like gamma correction, increase or suppress particular intensity ranges, while linear adjustments, like histogram equalization, disperse pixel intensities throughout the image. Contrast enhancement is helpful in genomics to bring out minute details in imaging data. For instance, contrast enhancement can highlight genetic traits or defects that may be important for diagnosis in chromosomal imaging or histopathology slides.

In genomics, these preprocessing methods are crucial for guaranteeing the accuracy and comprehensibility of imaging data. These methods improve contrast, lower noise, and handle pixel intensity variations to enhance the accuracy of later analysis like segmentation, feature extraction, and classification. The advancement of genomic imaging technology necessitates the continuous development and improvement of preprocessing techniques to extract relevant insights from various and complicated genomic datasets.

1.3.2 Segmentation Techniques

Segmentation is the process of partitioning an image into different regions or objects. The goal is to identify the extent and location of an object or area of interest in a snap. Segmentation techniques are commonly used in genomic research for identifying and isolating specific cell types, chromosomes, or regions in an image.

Thresholding:

thresholding is a primary segmentation method frequently used in genomics to identify pertinent elements in genomic pictures according to pixel intensity. In fluorescence microscopy images, for instance, thresholding aids in isolating regions of interest, such as nuclei or particular cellular structures, where various genetic components may be labelled with fluorescent markers.

Thresholding is a common technique in genomics that helps identify and quantify genetic components by separating signal from noise. This is especially useful for applications such as cytogenetic imaging and DNA microscopy, where the fluorescent signal’s strength indicates the existence of specific genetic material.

Watershed:

in genomics research, the watershed method divides entities with intricately linked boundaries. Watershed segmentation aids in the distinction of unique entities in genomic imaging, where discrete genetic components may be densely packed or overlapping. Watershed segmentation can help to accurately separate and characterize individual chromosomes or DNA strands in chromosomal imaging or DNA conformation studies. Studying genetic anomalies, structural changes, or the spatial configurations of genetic material within the nucleus depends on this.

Graph-based division:

use in genomics: graph-based segmentation is beneficial for managing big and intricate datasets in genomics. It uses graphs to depict genomic pictures, with pixels or areas acting as nodes and their interactions acting as edges. This method aids in capturing the connections and spatial correlations between genetic components. Graph-based segmentation considers the bonds and relationships between various genomic regions, which makes it easier to extract significant structures in applications using multi-dimensional genomic data, including 3D chromatin imaging or volumetric microscopy. This is essential for comprehending the three-dimensional architecture of the genome and researching higher-order chromatin organization.

The ability to recognize and isolate particular genetic features or patterns makes these segmentation approaches essential to genomic picture analysis. Their use helps with tasks including chromosomal abnormality characterization, gene expression pattern identification, and examining the spatial organization of genetic material within cells. The development and application of segmentation algorithms will be essential as genomics moves forward to extract relevant insights from large, complicated genetic datasets.

1.3.3 Feature Detection and Extraction

In the field of genomics, many methods for feature extraction and detection are essential for deciphering intricate structures and patterns found in biological images. These methods, each with a particular use, contribute substantially to thoroughly examining genetic data. The following are some essential techniques used in genomic research for feature extraction and detection:

Edge detection:

an essential method for determining the borders between various areas in an image is edge detection. This technique is instrumental in genomics for accurately drawing the boundaries of chromosomes, cellular structures, and other genetic elements. Edge detection helps capture small features essential for additional analysis and interpretation by emphasizing changes in pixel intensities.

Blob identification:

within genomic pictures, regions with comparable characteristics—like size or intensity—can be found using blob identification algorithms. This technique works well in genomics to isolate cellular structures or patterns with similar properties. Blob detection helps extract significant characteristics from microscopic images by improving the segmentation procedure.

Line detection:

line detection techniques identify straight lines in an image, frequently interpreted as structural features like tubes or fibers in tissue imaging. This technique is used in genomic research to clarify the linear configurations of genetic material or cellular structures. Line detection helps to characterize the structural organization of genetic components in microscopic images by identifying these linear patterns.

Object detection:

in genomic research, object detection methods are essential for locating particular entities in a picture, such as cells, chromosomes, or nuclei. These methods use sophisticated algorithms to identify and find pre-identified objects of interest. Accurate object detection is crucial in genomics for tasks like cellular structure analysis, genetic abnormality quantification, and understanding spatial genetic material organization.

Each of these methods for detecting and extracting features adds something unique to the diverse field of genetic research. Researchers can extract meaningful information from complicated genomic pictures through edge recognition, blob identification, line detection, and object recognition. The ongoing development and implementation of these techniques promise to yield more profound insights into the complicated field of genomics and increase our knowledge of genetic architecture and its consequences in health and illness, particularly as genomic technology progresses and datasets become more complex.

1.3.4 Classification Techniques

Once the features have been extracted, classification techniques can be used to group these features into different categories. The classification aims to assign labels to the segments based on their characteristics. There are many methods for classification and clustering available, and the choice of method depends on the type of data and the objectives of the analysis.

Decision tree

: recursive feature partitioning is the basis for classifying genetic data using decision trees in genomics. Decision trees are helpful in genomics for tasks including classifying various genetic mutations, determining gene expression patterns, and differentiating between normal and aberrant genomic profiles.

Researchers can comprehend the hierarchical principles utilized for classification using decision trees, which are interpretable models. In genomics, this transparency helps detect critical genetic traits that support particular sorts.

SVMs, or support vector machines:

based on genomic data, support vector machines (SVM) are extensively employed in genomics to perform tasks including classifying gene expression profiles, detecting biomarkers, and differentiating between distinct disease subtypes. Support vector machines work very well in genomic spaces with several dimensions.

In a high-dimensional feature space, SVM creates a hyperplane to divide classes. Because genomics datasets frequently contain many features (genes) compared to sample size, SVM’s capacity to identify the ideal separation hyperplane is helpful for precise classification.

Random forest

: random forest is used in genomics to improve the resilience and accuracy of classifications. It is frequently used for tasks including forecasting patient outcomes, classifying samples based on intricate genomic patterns, and discovering genetic markers linked to disease. Genomic relevance: random forest constructs several decision trees and uses voting to integrate the results. This ensemble technique improves the model’s ability to capture complex interactions within the data and reduces overfitting in genomics.

Neural networks:

DNA sequences, gene expression patterns, and chromatin structure are examples of complicated genomic data that neural networks are excellent tools for identifying. From massive genetic databases, they can decipher complex patterns and representations.

Neural networks are made up of linked layers that can recognize hierarchical data representations. Neural networks are helpful in genomics for applications like illness outcome prediction, cancer subtype classification, and genomic area annotation because they can capture nonlinear correlations.

These categorization methods are essential in genomics because they help researchers make sense of immense and complicated datasets. By making it easier to find patterns, biomarkers, and correlations in genomic data, their application advances our knowledge of the genetic basis of many biological phenomena as well as the diagnosis and treatment of disease. The application of cutting-edge machine learning algorithms will remain crucial in helping to extract insightful information from genomic data as genomics research advances.

1.4 A Journey Through Computer Vision for Detecting and Analyzing Genetic Patterns

Let us say you are interested in studying the genetic basis of Alzheimer’s disease. You could collect images of brain tissue from patients with Alzheimer’s disease and healthy controls [18, 19]. You could then use computer vision to analyze the photos for genetic patterns.

One way to do this would be to use image segmentation. Image segmentation is dividing an image into different regions based on their properties. In this case, you could use image segmentation to separate the brain tissue images into areas containing different cell types.

Once you have segmented the images, you could use feature extraction to extract features from each region. Features are measurements that can be used to describe the properties of an image region. For example, you could remove features such as the area’s average intensity, the power’s variance, and the texture of the part.

Finally, you could use machine learning to train a model to predict whether a brain tissue image is from a patient with Alzheimer’s disease or from a healthy control. The model would be trained on a dataset of ideas that have already been labelled as either “Alzheimer’s disease” or “healthy control.”

Once the model is trained, you could use it to predict the labels of new images. This would allow you to detect and analyze genetic patterns in brain tissue images without manually labelling the photos.

This is just one example of how computer vision can be used to detect and analyze genetic patterns. Many other techniques can be used; your specific approach will depend on the application.

1.5 Case Study

This study prepared fungal mycelia from colonies grown on potato dextrose agar at 18°C for 3–4 weeks. The mycelia were collected, cut into small pieces, and inoculated into a complete medium for further cultivation. After 10 days of incubation at 22°C with shaking, the mycelium was harvested, washed, freeze-dried, and stored. Subsequently, DNA extraction was performed on 20 mg of freeze-dried mycelium. The mycelium was ground to a fine powder and lysed in a lysis buffer. RNAse treatment was carried out by adding NaCl solution to precipitate unwanted components. The DNA-containing supernatant was purified with chloroform, phenol, and isopropanol. The resulting DNA pellet was washed with ethanol, dried, and dissolved in TE buffer for storage at –20°C. The extracted DNA was then subjected to polymerase chain reaction (PCR) using Taq polymerase, which amplified specific regions of interest. Polymerase chain reaction (PCR) utilizes thermal cycling to generate millions of copies of the target DNA sequence. This approach strengthens DNA fragments up to ∼10 kilobase pairs, facilitating further analyses and research on the fungal species under investigation.

This study generated a gel picture of fungal extracted DNA, explicitly targeting the 18S gene, through PCR amplification. The gel image displays the separated DNA fragments, allowing researchers to visualize the presence and size distribution of the amplified products. Subsequently, a polygenic tree (shown in Figure 1.6) was constructed using the genetic information obtained from the gel analysis (shown in Figure 1.5). This tree provides insights into the genetic relatedness and evolutionary relationships among the fungal species under investigation. By aligning the amplified 18S gene sequences and applying phylogenetic analysis, the tree illustrates the branching patterns, clustering the fungal isolates based on their genetic similarities. Combining the gel picture and the polygenic tree offers valuable information to identify and understand the diversity, taxonomy, and evolutionary history of the fungi in the sample. Such findings contribute to our broader knowledge of fungal ecology and can be crucial in various fields, including environmental monitoring, medical research, and biotechnological applications.

Figure 1.5 Gel picture for fungal extracted DNA (18S gene amplified fraction).

Figure 1.6 Phylogenetic tree.

1.6 Applications of Image Analysis in Genomic Research

The applications of computer vision techniques in genomic research are diverse and impactful, playing a vital role in various aspects of analysis and interpretation [16, 17]. Integrating computer vision in genomics opens new data analysis, visualization, and performance possibilities. Some of the critical applications of computer vision in genomics include:

One essential application is nuclear segmentation, which accurately delineates nuclei to identify and quantify cellular and subcellular structures. Numerous segmentation techniques discussed earlier have proven valuable in this context, aiding researchers in understanding cellular processes and interactions at a deeper level.

Chromosome analysis is another crucial area where computer vision plays a significant role. Using image analysis techniques, researchers can investigate chromosomal abnormalities and gain insights into chromosomal structure and function. This analysis includes examining various chromosome features, such as number, length, and morphology, providing essential information for genetic research and disease studies.

Cell morphology analysis is also advanced through image analysis techniques, allowing the quantification of cell shape, size, and nucleus-to-cytoplasm ratio. This analysis aids in identifying different cell types and enhances the understanding of cellular behavior, essential in areas like cancer research and developmental biology.

Furthermore, tissue microarrays, a widely used tool in genomic research for high-throughput analysis, benefit significantly from computer vision applications. Image analysis enables automated image acquisition, segmentation, and feature extraction in tissue microarrays, accelerating the investigation of gene expression, tissue architecture, and biomarker identification.

Genome assembly and annotation: computer vision techniques can aid in assembling fragmented DNA sequences obtained from NGS, helping to reconstruct complete genomes. Moreover, these methods can assist in the annotation of genes and other functional elements within the genome.

Image-based genomic analysis: advances in imaging technologies have enabled the visualization of genomes in a spatial context. For instance, chromatin conformation capture (3C) and related techniques provide 3D spatial maps of genomic interactions. Computer vision algorithms can analyze such images to identify genomic interactions and infer the 3D organization of the genome.

Gene expression profiling: microscopy images and imaging-based methods can be used to study gene expression at the single-cell level. Computer vision techniques can help quantify gene expression patterns and understand cellular heterogeneity.

Predicting genetic variations: computer vision approaches can contribute to predicting genetic variations, such as SNPs, insertions, and deletions, from genomic data.