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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
Multimodal Data Fusion for Bioinformatics Artificial Intelligence is a must-have for anyone interested in the intersection of AI and bioinformatics, as it delves into innovative data fusion methods and their applications in ‘omics’ research while addressing the ethical implications and future developments shaping the field today.
Multimodal Data Fusion for Bioinformatics Artificial Intelligence is an indispensable resource for those exploring how cutting-edge data fusion methods interact with the rapidly developing field of bioinformatics. Beginning with the basics of integrating different data types, this book delves into the use of AI for processing and understanding complex “omics” data, ranging from genomics to metabolomics. The revolutionary potential of AI techniques in bioinformatics is thoroughly explored, including the use of neural networks, graph-based algorithms, single-cell RNA sequencing, and other cutting-edge topics.
The second half of the book focuses on the ethical and practical implications of using AI in bioinformatics. The tangible benefits of these technologies in healthcare and research are highlighted in chapters devoted to precision medicine, drug development, and biomedical literature.
The book addresses a wide range of ethical concerns, from data privacy to model interpretability, providing readers with a well-rounded education on the subject. Finally, the book explores forward-looking developments such as quantum computing and augmented reality in bioinformatics AI. This comprehensive resource offers a bird’s-eye view of the intersection of AI, data fusion, and bioinformatics, catering to readers of all experience levels.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI
1.1 Introduction
1.2 Literature Review
1.3 Results and Discussion
Conclusion
References
2 Automated Machine Learning in Bioinformatics
2.1 Introduction
2.2 Need of Automated Machine Learning
2.3 Automated ML in Various Areas of Bioinformatics
2.4 Major Obstacles for Automated ML in Various Areas of Bioinformatics
2.5 Applications of Automated ML in Various Areas of Bioinformatics
2.6 Case Study 1
2.7 Conclusion and Future Directions
References
3 Data-Driven Discoveries: Unveiling Insights with Automated Methods
3.1 Introduction
3.2 Important Functions in Bioinformatics Include Data Mining and Analysis
3.3 Deep Learning in Bioinformatics
3.4 Challenges and Issues
3.5 Conclusion
References
4 Comparative Analysis of Conventional Machine Learning and Deep Learning Techniques for Predicting Parkinson’s Disease
4.1 Introduction
4.2 Symptoms and Dataset for PD
4.3 Parkinson’s Disease Classification Using Machine Learning Methods
4.4 Parkinson’s Disease Classification Using DL Methods
4.5 Conclusion
References
5 Foundations of Multimodal Data Fusion
Introduction
What is Multimodal Data Fusion in Bioinformatics AI?
Types of Data Modalities in Bioinformatics
Challenges and Considerations in Multimodal Data Fusion
Foundational Principles of Data Fusion
Machine Learning and Deep Learning Techniques for Multimodal Data Fusion
Feature Representation and Fusion
Applications in Bioinformatics AI
Evaluation Metrics and Validation Strategies
Ethical and Legal Considerations
Future Directions and Challenges
Conclusion
References
6 Integrating IoT, Blockchain, and Quantum Machine Learning: Advancing Multimodal Data Fusion in Healthcare AI
6.1 Introduction
6.2 Internet of Things (IoT) in Healthcare
6.3 Blockchain Technology in Healthcare
6.4 Quantum Machine Learning in Healthcare
6.5 Integration of IoT, Blockchain, and Quantum Machine Learning in Healthcare
6.6 Ethical and Regulatory Considerations in Healthcare Technology
6.7 Challenges and Future Directions in Healthcare Technology Integration
6.8 Results and Discussion
6.9 Conclusion
References
7 Integrating Multimodal Data Fusion for Advanced Biomedical Analysis: A Comprehensive Review
7.1 Introduction
7.2 Multimodal Biomedical Analysis
7.3 Challenges in Data Fusion
7.4 Deep Learning Methods for Data Fusion
7.5 Case Studies and Applications
7.6 Future Directions
7.7 Conclusion
References
8 Machine Learning Approaches for Integrating Imaging and Molecular Data in Bioinformatics
8.1 Introduction
8.2 Background and Motivation
8.3 Machine Learning Basics
8.4 Approaches for Data Integration
8.5 Machine Learning Techniques for Imaging and Molecular Data
8.6 Applications
8.7 Challenges and Future Directions
8.8 Case Studies
8.9 Conclusion
References
9 Time Series Analysis in Functional Genomics
9.1 Introduction
9.2 Foundations of Time Series Analysis in Functional Genomics
9.3 Methodologies for Time Series Analysis
9.4 Applications of Time Series Analysis in Functional Genomics
9.5 Integration with Multimodal Data
9.6 Conclusion
References
10 Review of Multimodal Data Fusion in Machine Learning: Methods, Challenges, Opportunities
10.1 Introduction
10.2 Related Work
10.3 Multimodal and Data Fusion
10.4 Applications, Opportunities, and Challenges
10.5 Conclusion and Future Directions
References
11 Recent Advancement in Bioinformatics: An In-Depth Analysis of AI Techniques
11.1 Introduction
11.2 AutoMLDL Methods
11.3 Application of AutoMLDL in Bioinformatics
11.4 Advanced Algorithm in AutoMLDL for Bioinformatics
11.5 Security and Privacy Issues in AutoMLDL
11.6 Conclusion and Future Works
References
12 Future Directions and Emerging Trends in Multimodal Data Fusion for Bioinformatics
12.1 Introduction
12.2 Foundational Concepts
12.3 Current State of Multimodal Data Fusion in Bioinformatics
12.4 Emerging Trends in Data Fusion
12.5 Algorithms
12.6 Future Directions
12.7 Case Studies and Applications
12.8 Challenges and Opportunities
12.9 Conclusion
References
13 Future Trends in Bioinformatics AI Integration
Introduction
What Is Multimodal Data Fusion?
Types of Multimodal Data in Bioinformatics
Challenges in Multimodal Data Fusion
Multimodal Data Integration Approaches
Feature Representation and Selection
Integration of Omics Data
Clinical Applications
Imaging Data Fusion
Biological Network Integration
Applications in Precision Medicine
Computational Tools and Resources
Future Directions and Challenges
Conclusion
References
14 Emerging Technologies in IoM: AI, Blockchain and Beyond
14.1 Introduction
14.2 Artificial Intelligence (AI) in Healthcare
14.3 Blockchain in the Medical Landscape
14.4 Benefits of Using Technologies in IoM
14.5 Integration of Cutting-Edge Technologies
14.6 Beyond AI and Blockchain: Exploring Additional Technologies
14.7 Ethical Considerations in Implementing Emerging Technologies
14.8 Conclusion
References
15 Natural Language Processing in Biomedical Literature
15.1 Introduction
15.2 History
15.3 Theoretical Foundation: Natural Language Processing in Scientific Writing
15.4 Sources of Diversity in Biomedical Literature’s Natural Language Processing
15.5 Disagreement and Conflict
15.6 Natural Language Processing Trends and Patterns in Biomedical Literature
15.7 Natural Language Processing’s Useful Applications in Biomedical Literature
15.8 Future Prospects of NLP in Biomedical Literature
15.9 Conclusion
References
16 Biomedical Research Enrichment Through Sentiment Analysis in Patient Feedback: A Natural Language Processing Approach
16.1 Introduction
16.2 Applications of NLP
16.3 Background Studies in Sentimental Analysis
16.4 Processes Needed for Sentimental Analysis
16.5 Conclusion
Acknowledgment
References
About the Editors
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Year-wise progress of Multimodal data fusion in bioinformatics AI.
Chapter 2
Table 2.1 Significance of AutoML in bioinformatics.
Table 2.2 Major challenges faced by automated machine learning (AutoML).
Table 2.3 Applications of AutoML in bioinformatics.
Chapter 3
Table 3.1 An examination of the many data mining approaches that are utilized ...
Table 3.2 An examination of a number of different deep learning approaches in ...
Chapter 4
Table 4.1 Symptoms for PD [16].
Table 4.2 Datasets available for PD classification
Table 4.3 Studies which utilized ML models to classify PD vs. healthy control ...
Table 4.4 Studies which utilized DL models to classify PD vs. healthy control ...
Chapter 7
Table 7.1 Comparative analysis of various research in the field of multimodal ...
Table 7.2 Comparative analysis of various challenges in data fusion and possib...
Table 7.3 Comparative analysis of various deep learning architectures and appl...
Table 7.4 A comparative analysis of various case studies of multimodal data fu...
Table 7.5 Future directions of multimodal data fusion.
Chapter 10
Table 10.1 A comparative analysis of existing research in multimodal research.
Table 10.2 Comparison of various fusion models used in multimodal.
Chapter 11
Table 11.1 Comparative analysis of existing research of AutoMLDL methods in Bi...
Table 11.2 Comparative analysis of applications of AutoMLDL methods in bioinfo...
Chapter 16
Table 16.1 Comparison of distinguishable datasets utilized in distinct article...
List of Illustrations
Chapter 1
Figure 1.1 Multimodal data fusion for bioinformatics AI.
Figure 1.2 Workflow of multimodal data fusion in bioinformatics.
Figure 1.3 Year-wise progress in multimodal data fusion for bioinformatics AI.
Figure 1.4 Various methodologies and their progress year-wise for multimodal d...
Chapter 2
Figure 2.1 Machine learning in bioinformatics.
Figure 2.2 Automated machine learning process.
Figure 2.3 Automated machine learning.
Figure 2.4 Automated ML in various areas of bioinformatics.
Figure 2.5 AI-based decision-making system.
Figure 2.6 Transcriptomics.
Figure 2.7 Computational biology.
Chapter 3
Figure 3.1 The characteristics of big data.
Chapter 6
Figure 6.1 Sequence of operations in healthcare applications enhanced by block...
Figure 6.2 Real-world applications of quantum computing.
Figure 6.3 Integrating blockchain into healthcare.
Figure 6.4 Blockchain-based healthcare data management.
Figure 6.5 Blockchain’s role in IoMT.
Figure 6.6 Use of multiple quantum filters in convolutional hybridization.
Figure 6.7 Quantum computing’s role in advancing precision medicine with multi...
Figure 6.8 Categorization of essential eechnologies for securing healthcare in...
Figure 6.9 Integration model of IoT, blockchain, and QML in healthcare.
Chapter 7
Figure 7.1 Multimodal applications in smart healthcare.
Chapter 8
Figure 8.1 Overview of learning methodologies.
Figure 8.2 Main anatomical features of the human brain.
Figure 8.3 Sequential steps in image processing.
Figure 8.4 Pre-processing strategies for brain MRI scans.
Figure 8.5 Health issues diagnosed with the help of ML techniques.
Figure 8.6 Classifier performance on a bioinformatics dataset.
Figure 8.7 Feature importance from a machine learning model.
Figure 8.8 Gene expression heatmap.
Figure 8.9 PCA biplot for dimensionality reduction.
Figure 8.10 Survival analysis using Kaplan-Meier curves.
Chapter 9
Figure 9.1 Outline of foundations of time series analysis.
Figure 9.2 Molecular concert in cells.
Figure 9.3 Methodologies for time series analysis.
Figure 9.4 Machine learning approaches.
Figure 9.5 Dynamic bayesian networks (DBNs).
Figure 9.6 Functional data analysis.
Chapter 10
Figure 10.1 Data fusion model.
Figure 10.2 The relationship of multimodal with machine learning in various se...
Chapter 11
Figure 11.1 Comprehensive analysis of important models of machine learning and...
Figure 11.2 The application of AutoMLDL in bioinformatics and coverage.
Chapter 12
Figure 12.1 Standard biometric system configuration.
Figure 12.2 (a) Sequential and (b) Concurrent system architectures.
Figure 12.3 Classification of biometric fusion levels.
Figure 12.4 Sensor-level fusion technique.
Figure 12.5 Feature-level data combination techniques.
Figure 12.6 Accuracy comparison of data fusion techniques.
Figure 12.7 Performance improvement through ensemble learning.
Figure 12.8 Dimensionality reduction visualization.
Figure 12.9 Computational time vs. data volume.
Figure 12.10 Real-world application success rate.
Chapter 14
Figure 14.1 Artificial intelligence in healthcare.
Figure 14.2 Benefits of using latest technologies in IoM.
Chapter 15
Figure 15.1 Biomedical text analysis using NLP.
Figure 15.2 Biomedical knowledge graph.
Figure 15.3 Clinical decision support systems.
Chapter 16
Figure 16.1 Subfields of artificial intelligence and applications of natural l...
Figure 16.2 Breakdown of research papers between 2019 and 2024.
Figure 16.3 Typical working flow of sentimental analysis process.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
About the Editors
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Multimodal Data Fusion for Bioinformatics Artificial Intelligence
Edited by
Umesh Kumar Lilhore
Abhishek Kumar
Narayan Vyas
Sarita Simaiya
and
Vishal Dutt
This edition first published 2025 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© 2025 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 9781394269938
Front cover images supplied by Pixabay.comCover design by Russell Richardson
Preface
This book addresses the fascinating intersection of artificial intelligence (AI) and bioinformatics. This book, divided into 16 comprehensive chapters, delves into how AI technologies are revolutionizing the analysis and integration of diverse biological data. It provides a balanced perspective on the latest research, practical applications, and the challenges encountered in combining multiple types of data for bioinformatics research and healthcare innovation.
Chapter 1, “Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI,” introduces how fusing various data types enhances research, yet poses significant challenges. Chapter 2 discusses the impact of Automated Machine Learning (AutoML) on bioinformatics, showcasing how automated processes simplify and accelerate research. Chapter 3 uncovers how AI-driven data analysis brings to light new biological insights, emphasizing its transformative potential.
Chapter 4 compares the effectiveness of Machine Learning and Deep Learning models, focusing on Parkinson’s disease prediction. Chapter 5 outlines essential concepts of data fusion in AI, explaining how integrating diverse information sources enhances outcomes. Chapter 6 examines integrating emerging technologies such as IoT, Blockchain, and Quantum Machine Learning in healthcare and discusses associated ethical issues.
Chapter 7 provides a thorough review of data fusion techniques in biomedical research, using case studies to illustrate real-world applications. Chapter 8 explores machine learning methods that integrate imaging and molecular data, expanding the possibilities for bioinformatics research. Chapter 9 explains the methods used in time series analysis in genomics, offering insight into genetic changes and their link to diseases.
Chapter 10 explores various machine learning approaches to data fusion, covering their roles in fields like diagnostics and human-machine interactions. Chapter 11 presents recent AI advancements in bioinformatics, including novel algorithms for disease research and drug resistance analysis. Chapter 12 highlights future trends in AI and data fusion, focusing on privacy-preserving methods and cutting-edge technologies.
Chapter 13 addresses AI’s role in precision medicine, demonstrating how integrating diverse medical data can enhance patient care. Chapter 14 discusses the Internet of Medicine, focusing on AI and Blockchain technologies and their potential to improve healthcare data security. Chapter 15 provides a simplified explanation of natural language processing (NLP) in analyzing biomedical literature, revealing how AI processes complex medical texts. Finally, Chapter 16 showcases how sentiment analysis in patient feedback can enrich medical research through advanced NLP techniques.
This book serves as a foundational guide for researchers, students, and professionals looking to understand and harness the power of AI-driven data fusion in bioinformatics, paving the way for future advancements in the field.
Dr. Umesh Kumar Lilhore
Galgotias University, Greater Noida, UP, India
Dr. Abhishek Kumar
Department of CSE, Chandigarh University, Mohali, India
Narayan Vyas
Department of Computer Science and Application, Vivekananda Global University, Jaipur, India
Dr. Sarita Simaiya
Galgotias University, Greater Noida, UP, India
Vishal Dutt
Department of CSE, Chandigarh University, Mohali, India
1Advancements and Challenges in Multimodal Data Fusion for Bioinformatics AI
Priya Batta
Department of Computer Science and Engineering, Chandigarh University, Mohali, India
Abstract
An artificial intelligence technique used in bioinformatics integrates multiple biological data sources to understand complex biological processes. The research mainly focuses on the discovery of fusion technologies and their associated challenges. Despite the significant progress made in machine learning algorithms, various issues such as scalability, interpretability, and regulatory still exist. Drug discoveries, accurate medicine, and systems biology are the three main sectors of application. Future research should focus on increasing scalability, increasing interpretability, and promotion of data standardisation. Thus, it will make it easier to combine multimodal data in a more effective way, which will advance medical care and biological research.
Keywords: Artificial intelligence (AI), multimodal data fusion (MDF), bioinformatics, genomics, drug discovery
1.1 Introduction
There are many AI techniques which are used for combining various types of data from different biological sources; this is known as Multimodal data fusion (MDF) for AI [1]. Transcriptomics, proteomics, metabolomics, and medical data are only a few of the modalities that are used in this method to improve biological understanding, disease diagnosis, and appropriate therapy [2, 3].
Figure 1.1 Multimodal data fusion for bioinformatics AI.
In bioinformatics AI, MDF (as shown in Figure 1.1) is used as follows:
Integration of Various Features: Various AI methods are implemented to combine features that have been gathered from multiple data modalities. In some cases, combining gene expression data with protein interaction networks or DNA sequences with clinical characteristics may provide a more detailed knowledge of biological processes
[4]
.
Neural Network Architectures: Deep Neural Networks (DNNs), Neural Networks with Recurrent Connections (RNNs), and Deep Belief Networks (DBNs) are a few machine learning models that can handle difficult multimodal data. Such architectures are capable of capturing intricate relationships between different kinds of data and using those connections to create their own representations [
3
,
5
].
Multimodal Embeddings: Automatic Encoders (AE) and Variational Automatic Encoders (VAEs) are two kinds of AI techniques employed for creating low-dimensional representations for multimodal feedback. These connections keep significant characteristics throughout modalities, which simplifies later tasks like classification, categorising, and regression [
2
,
6
].
Grid-based Fusion: Grid neural networks (GNNs) and other grid-based AI models are used for combining diverse biological networks. As these models include both node features and network topology, they can be helpful for exactly simulating connections within complicated biological systems, such as networks of gene regulation or protein-protein interaction networks
[7]
.
Transferable Learning: The method of transferring knowledge from one activity or data source to another is made easier by transfer learning methods. For particular bioinformatics applications, pre-trained AI models developed on huge data sets can be augmented by utilising knowledge from multiple sources and disciplines
[8]
.
Medical Decision Assistance: AI in bioinformatics has been applied to systems that help clinical decision-making through multimodal data integration. These systems can assist healthcare providers in identifying disorders, determining the best course of therapy, and predicting projections by combining healthcare data with genetic identification, imaging results, and other relevant data
[9]
.
Medicinal Development and Recycling: AI-driven MDF expedites these processes by merging biological structure, medical records, response profiles to medication, and cellular information. With this method, potentially novel drugs can be found, drug efficacy can be anticipated, and therapeutic benefits can be maximised
[10]
.
Medical Care: MDF enables personalised medical care by enabling methods that are based on the genetic profiles, medical features, and treatment outcomes of specific patients. Based on their analysis of multiple modalities, AI systems classify patients, estimate the possibility of disease, and propose specific therapies for every individual
[8]
. Essentially, multimodal data fusion in bioinformatics AI makes use of artificial intelligence capacity to combine different biological types of information and results in advancement in the discovery of drugs, medical care, and medical research.
Usage of Genomics Data: Genomics technology is used to generate very large data sets containing different biological components, such as proteins, DNA, and chemicals. Multimodal data fusion approaches enable the integration of omics data from several platforms, providing a greater awareness of biological systems and functions [
11
,
12
].
Study of Biological Systems: MDF permits the development and analysis of biological networks, particularly genetic regulation systems, interaction between proteins networks, and biochemical networks. By combining various types of genomics data, researchers are able to determine complex connections and functional connections inside the biological systems [
11
,
13
].
Disease Biomarkers Recognition: Combining many genomic and imaging data sets enables researchers to identify valuable biomarkers and genetic fingerprints associated with diseases. This enables the development of individualised treatment plans and the early diagnosis of patient conditions [
8
,
14
].
1.2 Literature Review
The state of MDF techniques has greatly advanced in the past few years. Deep Learning architectures have been used to improve traditional approaches such as Quantitative Fusion techniques [7, 10]. Combining data from various sources, including transcriptomics as well as these techniques, allows a more precise and deep knowledge of biological processes.
Various deep learning models [15] have shown remarkable capabilities in identifying various patterns from multimodal data. Graph-based fusion methods take advantage of the natural connections between biological components to model interactions, while ensemble learning techniques incorporate several models to improve the accuracy of predictions and standardisation.
Uses: MDF is used in various fields within bioinformatics AI. Personalised therapies are developed with the identification of disease subgroups and biomarkers in disease prognosis and diagnosis, which are made possible by the fusion of biological, transcriptomic, and imaging information. The creation of novel medicines is accelerated through the use of omics data integration in drug discovery, which makes target identification, drug repurposing, and drug response prediction easier [10]. Moreover, multimodal fusion is essential to precision medicine because it combines genetic profiles with patient-specific clinical data to customise therapy regimens and forecast treatment results. Reconstructing molecular pathways and regulatory mechanisms in systems biology allows for the integration of omics data with biological networks, revealing information on drug interactions and disease [13] processes.
Obstacles: Multimodal data fusion in bioinformatics AI has a number of obstacles in spite of its potential. Integrating heterogeneous data sources with different modalities, resolutions, and noise levels is a major problem. Maintaining compatibility and interoperability across various data types is still a crucial problem that calls for effective pre-processing and harmonisation methods [14].
Year-wise progress is shown as follows:
2018: The foundation for combining various data modalities in bioinformatics AI was established with the advent of fundamental fusion algorithms
[5]
. The restricted scalability of these strategies for huge datasets was one of the main obstacles faced, though.
2019: An important development was the use of machine learning for fusion activities. Nonetheless, problems with the models explainability and interpretability were apparent as significant holes.
2020: New potential for managing complicated biological data were presented by the development of graph-based fusion techniques. However, there were still issues in efficiently handling.
2021: Improving fusion performance through the use of ensemble learning approaches showed potential. Assessing the effectiveness of fusion models is still hampered by the absence of uniform evaluation metrics.
2022: While successfully merging temporal
[11]
and geographical data faced difficulties, the use of attention mechanisms in fusion algorithms was a noteworthy development.
2023: While fusion performance was enhanced by the incorporation of transfer learning techniques, permission and data privacy became more significant ethical problems.
2024: While new opportunities were created by the investigation of reinforcement learning in fusion tasks, interoperability between various data sources remained a major obstacle [
4
,
8
].
Table 1.1 shows the year-wise progress of Multimodal data fusion in bioinformatics AI from 2018 to 2024 with methodology employed and gaps also.
Table 1.1 Year-wise progress of Multimodal data fusion in bioinformatics AI.
Year
Methodology used
Gaps
2018
[5]
Omics data integration for customised healthcare
Managing high-dimensional heterogeneous data
2019 [
16
,
17
]
Developments in the architectures of deep learning
Fusion models’ interpretability and explainability
2020
[7]
Emergence of techniques to graph-based fusion
Integration of temporal and spatial data
2021 [
10
,
12
]
Use of multimodal fusion in the search for new drugs
Difficulties managing the heterogeneity and scalability of data, data privacy.
2022
[15]
Multimodal fusion algorithms advancements
Absence of uniform measures for evaluation
2023
[2]
Multimodal data integration in clinical contexts
Restricted compatibility between several data sources
2024
[3]
Multimodal fusion development for uncommon disorders
Pre-processing and data harmonisation challenges
Furthermore, there are difficulties with the interpretability and explainability of fusion models, especially in deep learning-based methods where the intricacy of neural networks makes it difficult to comprehend feature interactions and decision-making procedures. Furthermore, because biomedical data is sensitive, ethical concerns about data protection, privacy, and informed permission are critical.
Workflow of Multimodal Data Fusion in Bioinformatics
The process of multimodal data fusion in bioinformatics AI is depicted in this diagram (Figure 1.2), starting from the original data sources and ending with the creation of insights. This design can be expanded and altered to include certain data modalities, pre-processing procedures, AI models, and analysis methods that are applicable to your application are shown in Figure 1.2.
Figure 1.2 Workflow of multimodal data fusion in bioinformatics.
Gathering of Data: Data are gathered on the genome, transcriptome, proteome, metabolome, and phenotype. These data types include a variety of biological informational components, including observable features (phenotypic), protein abundance (proteomic), RNA expression levels (transcriptomic), DNA sequences (genomic), and metabolite concentrations (metabolomics).
Pattern Pre-processing & Extraction: Selection of features, standardisation, and reduction of noise are common methods of preprocessing that are used on many kinds of data. Following pre-processing, results are drawn from the raw data utilising feature extraction methods. These characteristics serve as input for the next fusion process.
Integration of Multimodal Data: Methods for data fusion combine features that have been drawn from several data sources. During this fusion process, input from several sources gets combined to create one model that includes the supportive information accessible in each mode. Machine learning models such as neural networks or combination methods are utilised for fusion tasks.
Analysing downstream: Various downstream analysis tasks are carried out using the fused feature representation that is derived from the fusion process.
Classification: Samples are categorised into distinct groups, such as disease vs. healthy or distinct disease subgroups, using the fused features.
Regression: To estimate continuous variables, such as predicting the course of a disease based on biomarkers, predictive models are constructed.
Clustering: To aid in the identification of biomarkers or patient stratification, unsupervised learning techniques are utilised to cluster samples that share comparable attributes.
Association Analysis: Using statistical techniques, one can anticipate medication responses based on genetic profiles or find connections between molecular traits and clinical outcomes.
The above diagram (Figure 1.2) describes the various steps involved in multimodal data fusion in bioinformatics, following gathering information and pre-processing, such as extraction of features, fusion, and later analysis. For the integration of various biological data sources to support clinical decision-making, biomedical research, and the gathering of knowledge about complicated biological processes, each step of the workflow is important [18–21].
1.3 Results and Discussion
Researchers develop the foundation by looking into fundamental fusion methods including statistical techniques to integrate multiple information modalities in bioinformatics AI. Early studies focus on understanding the advantages and challenges of multimodal data fusion, such as variation in information as well as complexities.
As machine learning gains popularity, scientists are starting to look into how it might be used [5, 7, 15, 16, 19, 20, 22] in multimodal fusion for bioinformatics AI. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two deep learning architectures, are studied for their capacity to process complex multimodal data and extract various as well as significant features of multimodal data.
Figure 1.3 Year-wise progress in multimodal data fusion for bioinformatics AI.
The year-wise progress in multimodal data fusion for bioinformatics AI is shown in Figure 1.3. The advancement of bioinformatics multimodal data fusion techniques AI is an ongoing process characterised by small steps forward, cross-disciplinary cooperation, and the incorporation of state-of-the-art methods from machine learning and other related domains.
Figure 1.4 Various methodologies and their progress year-wise for multimodal data fusion for bioinformatics AI.
Graph-based fusion techniques as shown in Figure 1.4 are being used by researchers to take use of the interdependencies and linkages that are naturally present in biological entities. To capture intricate interactions inside biological networks and increase fusion accuracy, graph neural networks (GNNs) and graph convolutional networks (GCNs) are investigated. To improve the fusion process, attention mechanisms are added, which selectively focus on pertinent [17, 23] data from various modalities. Attention-mechanism-equipped models dynamically modify their attention weights according to feature relevance, improving interpretability and fusion performance.
To capitalise on the complementary advantages of various techniques, hybrid systems that integrate numerous fusion methodologies—for example, reinforcement learning with attention mechanisms or deep learning with graph-based methods—are being investigated. The creation of novel fusion approaches suited to the particular difficulties of bioinformatics AI is accelerated by interdisciplinary collaboration between academics in the domains of machine learning, bioinformatics, and other sciences.
In general, the development of multimodal data fusion strategies for bioinformatics AI is an ongoing process characterised by small steps forward, cross-disciplinary cooperation, and the incorporation of state-of-the-art methods from related domains such as machine learning.
Conclusion
Bioinformatics AI multimodal data fusion has made great strides, combining several data modalities to clarify intricate biological processes. Although advances have been made in fusion approaches and machine learning techniques, issues with standardisation, interpretability, and scalability still exist. Closing these gaps is essential to achieving multimodal fusion’s full promise in drug development, disease processes, and precision medicine. Future developments will be fueled by interdisciplinary cooperation and creative thinking, making it possible to integrate multimodal data more successfully for better clinical and biological research.
The creation of interpretable fusion models, the integration of temporal and spatial data, the development of federated learning techniques to address data privacy concerns, and the standardisation of benchmark datasets and evaluation metrics are some future research directions in multimodal data fusion for bioinformatics AI. To overcome obstacles and realise the full promise of multimodal data fusion for advancing biomedical research and enhancing healthcare outcomes, researchers, clinicians, and policymakers must work together.
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Note
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2Automated Machine Learning in Bioinformatics
Pushpendra Kumar1*, Gagan Thakral1, Vivek Kumar2 and Upendra Mishra1
1Department of Computer Science and Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, UP, India
2Department of Computer Science and Engineering, MIET, Meerut, UP, India
Abstract
The applications of machine learning are gaining popularity in various fields day by day. Bioinformatics is one of the fields in which automated machine learning, AutoML, has great potential in the future. AutoML can be used to create the predictive models and find certain patterns in biological data. Machine learning can be used as a tool for decision making and analysis of biological data. In this work, we have focused on how AutoML pipelines can be integrated into bioinformatics processes and emphasize how it can be used for tasks like drug discovery, protein structure prediction, and sequence analysis. Further, there are a few limitations associated with AutoML in bioinformatics, such as data scarcity, heterogeneous data, interpretability problems, and scalability problems. In conclusion, the future directions and possible developments in AutoML techniques for bioinformatics are discussed, highlighting how the field of artificial intelligence and machine learning can build sustainable technologies using machine learning for bioinformatics applications.
Keywords: Automated machine learning, bio-informatics, genomics, proteomics, deep learning
2.1 Introduction
In recent years, advancements in technologies have allowed us to capture loads of complex data including the domains of proteomics, metabolomics, imaging, and genomics. To analyse this data and get insights from it is a challenging task. Even this data can be further used for intelligent predictions and decision-making. Conventional bioinformatics data processing techniques have their own limitations; they are labour intensive, require manual interference, and are biased. This huge data bring both challenges and opportunities for researchers. From the field of artificial intelligence, machine learning is such a useful tool to use this data constructively and make automated predictions in bio informatics as shown in Figure 2.1.
Automated machine learning, also coined as AutoML [1, 2] is a technique which requires no human intervention to process the bioinformatics data. We can derive useful insights and predictions for the bioinformatics applications with the help of AutoML. The entire machine learning pipeline starting from data cleaning, feature engineering, model selection, hyperparameter tuning, data preparation, processing, predictions, insights and decision-making can be made automated with AutoML applications.
This chapter explores various approaches in bioinformatics [3] benefited by AutoML. Further, various problems faced in the task of applying machine learning in bioinformatics, including noise, data scarcity, interpretability, and heterogeneity of data are explored. This chapter also discusses how AutoML techniques might help to counter these difficulties. This chapter explores the use of Big Data [4] with AutoML for various bioinformatics applications such as customized medicines, drug discovery, protein structure and sequence analysis. There are several new discoveries in the area of life sciences and bioinformatics that are accelerated with the advancements of the latest machine learning technologies. AutoML could help us in making more complex biological interpretations [5] and hypotheses by automating time-consuming and repetitive manual procedures.
Figure 2.1 Machine learning in bioinformatics.
A. Challenges of Automated Machine Learning Approaches in Bioinformatics
There are several challenges faced by Machine Learning (AutoML) in processing biological data. The data collected from biological activities has particular qualities. This data is generally very complex in nature, variable and inconsistent. This data may vary in a wide range such as protein structures, genome sequences and medical records. There can be noise and inconsistencies in the data which are very common to biological data. Such challenges can be an obstacle for conventional machine learning techniques. The second issue with AutoML in bioinformatics can be the selection of model. The model must be interpretable and self-explainable since full openness of decision-making processes and transparency is required. Huge and complex datasets require a lot of processing resources. Another requirement is scaling, since the amount of data could rise with time. Various computing parallelization and optimization techniques can be very useful.
B. Advantages of Automated Machine Learning Approaches in Bioinformatics
There are several benefits of applying AutoML in bioinformatics. Transparency and minimization of human intervention, simplified model-building process, powerful insights, interpretations, reproducibility and standardization are major benefits of Automated machine learning in bioinformatics. The capability of processing a huge amount data in minimal time [6], building automatic machine learning pipelines for interpretable and self-explainable models, and fetching insights for effective and efficient decision-making could be very useful. AutoML frameworks provide optimal model performance by managing the challenges of hyperparameter tuning and feature engineering, thereby relieving researchers of some of their workload. AutoML enhances the reproducibility and openness by standardizing [7] the documentation and machine learning workflow, encouraging open scientific principles, and making it easier to validate and replicate findings across other investigations.
C. Process of Automated Machine Learning
There is a basic difference between conventional machine learning and automated machine learning processes. There is an automatic algorithm selection step. This step automatically selects a model based on the optimal outcome. The following figure displays the process of automated machine learning.
2.2 Need of Automated Machine Learning
There are several reasons why Automated Machine Learning (AutoML) is necessary. These reasons all add to the need for automated solutions in the fields of data analysis and machine learning. The following salient points underscore the necessity of AutoML:
Complexity of Machine Learning Pipeline:
Numerous complex procedures
[8]
, such as feature engineering, model selection, hyperparameter tweaking, and data preprocessing are involved in traditional machine learning workflows. Model development takes less time and effort when these procedures are automated as shown in
Figure 2.2
.
Scarcity of Data Science Experts:
There is a dearth of qualified data scientists that are adept at machine learning methods as well as subject expertise. By making it possible for academics and subject matter experts with little experience with programming or statistics to efficiently use sophisticated analytical tools, AutoML democratizes machine learning.
Rapidly Growing Data Volumes:
Large datasets are becoming too enormous to handle by hand as big data spreads across many areas, including bioinformatics. Scalable solutions for effectively processing and analyzing large amounts of data are offered by AutoML as shown in
Figure 2.3
.
Figure 2.2 Automated machine learning process.
Table 2.1 summarizes the significance of AutoML in bioinformatics. We can summarize the significance of the automated machine learning as follows.
Rapid Model Deployment:
Rapid deployment of AutoML make them most suitable and viable. Organizations can quickly implement predictive analytics solutions because of AutoML’s ability to speed up the model building process.
Reproducibility and Transparency:
AutoML frameworks standardize the model construction process and provide thorough documentation for each stage. This process helps to enhance reproducibility and transparency. This ensures the validity of scientific results by guaranteeing that tests can be repeated and that the results can be understood.
Resources Optimization:
Hyperparameter tuning and model selection are two resource-intensive operations. AutoML automates these processes and maximizes computational resources
[9]
. Financial savings by implementing machine learning solutions can be achieved with better use of computer resources.
Adaptation to Dynamic Environments:
Manual model retraining and adaption can be difficult as the amount of data keep growing with the time. AutoML setups are capable to provide real-time learning and updating the model. This ensures the adaptation of machine learning models in dynamic environments.
Figure 2.3 Automated machine learning.
Table 2.1 Significance of AutoML in bioinformatics.
How AutoML addresses features in bioinformatics
Data Collection & Interpretation
AutoML streamlines data collection by automating data preprocessing, cleaning, and integration from diverse biological sources. Machine learning steps of data cleaning feature extraction and selection can be automated.
Economical & High Productivity
No or minimal human intervention helps in saving time and resources. Rapid model development, higher productivity can be achieved with AutoML.
Fair & Impartial Decision-Making
Fairness, Transparency, impartiality, standardization and reproducibility can be ensured by applying AutoML.
No Human Error & Risk
Minimizing human error, automating repetitive tasks, optimizing model performance. Eventually, a robust model.
Availability
High availability as open-source libraries and commercial products. Provides wide range of easy-to-use and efficient tools.
2.3 Automated ML in Various Areas of Bioinformatics
The major areas in bioinformatics where AutoML can be applied are genomics, functional genomics, structural bioinformatics, computational biology, metabolomics, transcriptomics and pharmacogenomics as shown in Figure 2.4. AutoML can be applied to develop AI based Decision system in these areas as shown in Figure 2.5.
Genomics: Large genomic datasets can be processed and pipeline automatically with the help of automated machine learning. Automated Machine Learning (AutoML) has completely changed the field of genomics [10]. Identification of genetic variants and gene function prediction can be automated with machine learning. In the field of genomics the comprehension behind the complex genetic disorders and diseases can be made with AutoML. Significant and dynamic features in genomic data can be interpreted easily with these models. These models can continuously learn from the dynamic data generation. The models need to be adaptive, especially in the field of genomics.
Proteomics: The research related to protein structures in bioinformatics is known as proteomics. These structural data of proteins can be analyzed and modelled [11] easily with the help of AutoML. The integration of diverse data sources, the interpretation of sizable proteomic datasets, and the requirement for reliable algorithms can be achieved by this technology. Sparse and noisy data are some of the major challenges that still need to be overcome in proteomics. However, machine learning can be applied for the creation of predictive models for the prediction of protein structure and function, the discovery of biomarkers for the diagnosis and prognosis of diseases. The investigation of protein-protein interactions and signaling pathways can be applied. Proteomics researchers can get new insights into the molecular pathways underlying health and illness. This can be used in personalized medicine and focused therapeutic interventions.
Figure 2.4 Automated ML in various areas of bioinformatics.
Figure 2.5 AI-based decision-making system.
Transcriptomics: The study of an organism’s transcriptome, which is the complete set of RNA transcripts, is known as Transcriptomics as described in Figure 2.6[12]. It has become possible to analysis huge transcriptomics datasets with the advancements of computational technologies. We can bring valuable insights from this data. We can use AutoML to handle heterogeneous and noisy data for this purpose. The prediction of gene expressions patterns can be achieved. The identification of novel RNA biomarkers for the diagnosis and treatment of disease are some tasks that can be addressed with the help of AutoML.
Metabolomics: Analysis of complex metabolic patterns is known as Metabolomics [13]. Accurate and fast analysis of complex metabolic patterns can benefit from the addition of machine learning. Some challenges faced by applications in metabolomics are data preprocessing, metabolite identification, and handling the inherent unpredictability. Metabolomic data is related to the automation of machine learning processes in the metabolomics. The scope for machine learning is huge in metabolomics. We can perform identification of metabolic signatures linked to medication response and toxicity. We can also get clarification of metabolic pathways, and the identification of biomarkers for illness diagnosis and prognosis. New insights can be gained into metabolic control and biomolecular interactions, and metabolic phenotypes.
Figure 2.6 Transcriptomics.
Structural Bioinformatics: Structural bioinformatics [14] is another area in bioinformatics where machine learning can be applied. We can build more accurate and efficient protein structure analysis and prediction with AutoML. There are a few problems associated with data collection. The lack of good training data and computational complexity are common limitations. The interpretation of structural predictions is the main challenge faced. It can be used for drug design, protein-ligand interaction and protein structure prediction. New protein structures and interactions can be identified more quickly by utilizing machine learning techniques. The applications of these frameworks are medication development, protein engineering, and the comprehension of complicated biological systems.
Systems Biology: Machine learning can be applied in systems biology [14]. AutoML enables the integration and analysis of many biological data types such as proteomics, metabolomics, transcriptomics, and genomes. Systems biology can benefit greatly from machine learning. This potential includes developing predictive models for biological networks and recognizing emergent characteristics in intricate biological systems. The clarification of the connections between genotype and phenotype can be understood and better insights into living systems and developing applications for synthetic biology provided. Customized medicine and biotechnological process optimization by utilizing machine learning can be obtained.
Microarray: A microarray is a multiplex lab-on-a-chip [27]. These are two-dimensional arrays. We use a microarray to detect thousands of biological interactions. We can keep them on a solid substrate. Generally, it is a glass slide or silicon thin-film cell. We can assay and test large amounts of biological material using high-throughput screening, miniaturized, multiplexed, and parallel processing and identification techniques. There can be several types of microarrays such as protein microarrays, DNA, RNA, Microchip, cellular and antibody microarrays. These are very large and complex in nature. Manual analysis of these microarrays is a tedious task. Automated machine learning can help us in this task. We can use machine learning techniques like Bayesian classification, decision trees, random forests, and deep learning for the analysis of microarrays.
Computational Biology: As described in Figure 2.7, computational biology [15] has a great scope for AutoML, especially incorporating large datasets. The processing and interpretation of large, complicated biological data sets can be achieved with machine learning pipelines. Structural biology, statistics, biochemistry, physical chemistry, molecular biology and control theory are the main fields of computational biology where we can apply automated machine learning. This scope includes the ability to predict the structure and function of proteins, identify genetic variants linked to disease, and integrate multi-omics data to provide comprehensive biological insights.
Other areas: Other important areas in bioinformatics involving AutoML include functional genomics [16], evolutionary biology and pharmacog-enomics. Functional genomics allows for very accurate predictions of gene functions and regulatory relationships. Evolutionary biology [17] is the study of evolutionary processes and patterns in a wide range of species. Pharmacogenomics [18] provides customized medication dosing based on genetic variables. All these areas can be applied with AutoML frameworks.
Figure 2.7 Computational biology.
2.4 Major Obstacles for Automated ML in Various Areas of Bioinformatics
The biological operations generate a vast amount of data. We need the help of automation and machine learning to process this much data. Not only the amount of the data but also the heterogeneity of the data is a challenge. Table 2.2 summarizes the various challenges faced by the AutoML. This data is in unrefined form and needs further cleaning and preprocessing. Also, we need to choose the correct machine learning model automatically.
Table 2.2 Major challenges faced by automated machine learning (AutoML).
Challenges
Details
Data Heterogeneity
Variety of data kinds, including protein structures, genetic sequences, and clinical information.Makes feature engineering and data preprocessing difficult.
Interpretability
Some AutoML models are not interpretable.Difficult to draw conclusions from intricate biological data.Lack transparency.
Scalability
Scalability problems.Not able to deal with large bioinformatics datasets.
Domain-specific Knowledge
Lack of domain-specific knowledge.Incorporating domain knowledge into AutoML Framework might be difficult.
Noise and Missing Data
Missing and null values.Noisy data.Ineffective handling of noise.
Processing Resources
High Computational demands.Effective use of resources (optimization, parallelization).
Ethics and Regulation
Sensitive data, including genetic information and medical records.Model creation and deployment are made more difficult by the need for AutoML algorithms to abide by legal and ethical constraints
[19]
for data protection and privacy.
Validation and Reproducibility
Ensuring the validity and reproducibility
[20]
of machine learning models.
2.5 Applications of Automated ML in Various Areas of Bioinformatics
Several uses of Automated Machine Learning (AutoML) in diverse bioinformatics domains include (as summarized in Table 2.3):
Table 2.3 Applications of AutoML in bioinformatics.
AREA
Application
Genetics
Regulatory components and gene functions predicted through modelling.
Studies on genotype-phenotype associations and variant calling.
Analysis of gene expression and transcriptome profiling.
Investigations of genome-wide associations (GWAS)
[21]
for the examination of complicated traits.
The proteomics
Predicting and folding the structure of proteins.
Detection of protein-protein interactions and post-translational modifications (PTMs)
[22]
.
Proteome profiling and biomarker identification for prognosis and disease diagnosis.
Functional annotation and subcellular localization of proteins predicted.
The study of transcriptomics
Differential gene expression profiling in response to various therapies or circumstances.
Predicting isoforms and alternative splicing.
Analysis of gene co-expression and inference from regulatory networks.
Classification and functional annotation of long noncoding RNAs (lncRNAs).
The study of metabolism
Identification and annotation of metabolites.
Flux analysis and reconstruction of metabolic pathways.
Identification of biomarkers for metabolic profiling and illness diagnosis.
Drug metabolism pathways and metabolic phenotype prediction.
Biology of Systems
Reconstruction of biological networks, including metabolic and gene regulatory networks.
Modelling of system-level behavior and dynamic biological processes
[23]
.
Multi-omics data integration for a thorough systems-level investigation.
Prediction of cellular responses to perturbations and interactions between drugs and targets.
Employing Functional Genomics
Gene and non-coding region functional annotation.
Prediction of biological pathways and concepts from the Gene Ontology
[24]
(GO).
Finding transcription factor binding sites and gene regulatory elements.
Genetic variation annotation and implications for function.
Biology of Evolution
Evolutionary divergence study and reconstruction of phylogenetic trees.
Identification of adaptive evolution and positive selection.
Studies on molecular evolution and comparative genomics.
Ecological connections and species distribution forecasts.
Pharma-cogenomics
Genetic variation-based personalized medication response prediction.
Finding pharmacogenetic indicators for the toxicity and effectiveness of drugs.
Predicting polypharmacology and repurposing drugs.
Creation of personalized treatment plans using precision medicine techniques
[25]
.
2.6 Case Study 1
Title: Automated Machine Learning for Predictive Modelling of Protein-Protein Interactions [26].
Problem Statement: Decoding physiological processes and creating innovative therapeutic interventions require a thorough understanding of protein-protein interactions (PPIs). The complexity of protein structures and functions along with large search space makes it difficult to analyse and predict. The aim is to create an automated machine learning process that uses structural characteristics and protein sequence to predict PPIs.
Method:
Data collection: We need to compile huge datasets available publicly. These datasets can be downloaded from platforms like BioGRID and STRING that include known protein-protein interactions.
Feature extraction is the process of identifying important characteristics. Examples of such features are makeup of amino acids, physicochemical characteristics, and structural motifs, from protein sequences and structural data.
Preprocessing: We need to divide the dataset into training, validation, and test sets. Then clean the data, handle missing values and normalize the features.
Model Choice: We need to select suitable ML algorithm and tune hyperparameters. Ultmately, AutoML framework like TPOT or Auto-Sklearn can be used.
Model Training: Cross-validation is applied to guarantee robustness and generalization, upon the chosen models on the training set.
Model Evaluation: Metrics like accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC-ROC) to assess how well the trained models performed on the validation set can be used.
Model Testing: Evaluate the final model’s performance. Use test dataset to measure its capacity for generalization and accuracy in prediction.
Interpretation: To determine the essential characteristics and trends influencing protein-protein interactions, interpret the trained models.
Results: The model’s interpretability helped to generate new hypotheses. A highly accurate and predictive model for PPIs is successfully created using the automated machine learning pipeline. The model showed that it could generalize to previously untested data and outperform other approaches. The biological research can be enhanced by outlining the underlying mechanisms governing PPIs.
Case Study 2:
Title: Ribosomally synthesized and post-translationally modified peptides or RiPPMiner [28].
Problem Statement: Decoding of chemical structures. It contains more than 20 subclasses. A variety of organisms, including prokaryotes, eukaryotes, and archaea, produce RiPPs, which possess a wide range of biological functions.
Method:
Data collection: It has an inbuilt database RiPPDB database.
Components: This is a bioinformatics tool based on automated machine learning. You can use it for genome mining-based RiPP chemical structure decoding. RiPP has two main components: the RiPPDB database and the RiPPMiner web server. RiPPMiner is a quary interface. RiPPMiner finds 12 subclasses of RiPPs by guessing where the leader peptide will cut and where the last cross-link will be in the RiPP chemical structure.
Results: With the help of genome sequences, RiPPMiner is a special tool that can predict the intricate chemical structures of several kinds of RiPPs. The model provides a simple, easy to understand and operate user interface. Complex analysis can be done very easily with the help of this tool.
