Deep Learning Applications in Translational Bioinformatics

1st Edition, Volume 15 - March 7, 2024
Editors: Khalid Raza, Debmalya Barh, Deepak Singh, Naeem Ahmad
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 2 2 9 9 - 3
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 2 2 9 8 - 6

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basi… Read more

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Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

Cover image
Title page
Table of Contents
Series Page
Copyright
List of contributors
About the editors
Chapter 1: Deep learning ensembles in translational bioinformatics
Abstract
1.1 Introduction
1.2 Basic ensembling methods
1.3 Application
1.4 Extensions of the ensemble method in bioinformatics
1.5 Ensemble theory’s application to feature selection
1.6 Ensemble feature selection methods and stability of feature selection algorithms in bioinformatics
1.7 Algorithmic ensembles for feature selection
1.8 Conclusion
References
Chapter 2: Recursive feature elimination and multisupport vector machine in healthcare analytics
Abstract
2.1 Introduction
2.2 Related works
2.3 Proposed system
2.4 Results and discussion
2.5 Conclusion
References
Chapter 3: Sensor-enabled biomedical decision support system using deep learning and fuzzy logic
Abstract
3.1 Introduction
3.2 Literature review
3.3 Methodology
3.4 Results from experiment
3.5 Discussion
3.6 Conclusion
References
Further reading
Chapter 4: Prediction of Alzheimer’s disease using densely convolutional neural network
Abstract
4.1 Introduction
4.2 Literature review
4.3 Methodology
4.4 Materials and tools
4.5 Results and discussion
4.6 Conclusion
References
Chapter 5: Brain tumor detection from magnetic resonance imaging images using shallow convolutional neural network
Abstract
5.1 Introduction
5.2 Related works
5.3 Methodology
5.4 Result and discussion
5.5 Conclusion
References
Chapter 6: Multiview learning with shallow 1D-CNN for anticancer activity classification of therapeutic peptides
Abstract
6.1 Introduction
6.2 Related work
6.3 Methodology
6.4 Experimental setup
6.5 Conclusion
References
Chapter 7: Deep learning methods for protein classification
Abstract
7.1 Introduction
7.2 Literature survey
7.3 Protein representation methods
7.4 Proposed methodology
7.5 Result and discussion
7.6 Conclusion
References
Chapter 8: Biosensors-based identification of antibiotic resistance in bacteria
Abstract
8.1 Introduction
8.2 Gonnococal antibiotic resistance
8.3 Proposed work
8.4 Experimentation
8.5 Results and discussion
8.6 Conclusion
References
Chapter 9: Deep learning for vehement gene expression exploration
Abstract
9.1 Introduction
9.2 Gene expression
9.3 Gene expression analysis
9.4 Gene expression functioning
9.5 Gene expression technologies
9.6 Microarray technique
9.7 Strengths and limitations of microarray
9.8 Strengths of microarrays
9.9 Limitations of microarrays
9.10 Microarray functioning
9.11 RNA-Seq technique
9.12 Strengths and limitations of RNA-Seq
9.13 Strengths of RNA-seq
9.14 Limitations of RNA-seq
9.15 RNA-Seq functioning
9.16 Study on RNA-Seq over microarray
9.17 Deep learning
9.18 Deep learning sets unique from neural network
9.19 Deep learning sets unique from machine learning
9.20 Deep learning architectures: convolutional neural network and recurrent neural network
9.21 Convolutional neural network functioning
9.22 Recurrent neural networks functioning
9.23 Deep learning for gene expression
9.24 Convolutional neural networks for gene expression
9.25 Recurrent neural network for gene expression
9.26 Potential of recurrent neural network over convolutional neural network for gene expression
9.27 Conclusion
References
Chapter 10: Machine learning-enforced bioinformatics approaches for drug discovery and development
Abstract
10.1 Introduction
10.2 Drug discovery: an introduction and historical perspective
10.3 Machine learning approaches
10.4 Machine learning-enforced bioinformatic tools for drug discovery
10.5 Deep machine learning
10.6 Artificial intelligence
10.7 Machine learning-, deep learning-, and artificial intelligence-enforced bioinformatics and cheminformatics tools
10.8 Challenges and opportunities
10.9 Conclusion
References
Chapter 11: Role of deep learning in predicting drug formulations and delivery systems
Abstract
11.1 Introduction
11.2 Databases, deep learning tools and techniques
11.3 Data processing methods
11.4 Artificial intelligence algorithms and models in dosage form development
11.5 Conclusions and future prospects
References
Chapter 12: Deep learning in computer-aided drug design: a case study
Abstract
12.1 Introduction
12.2 Role of deep learning in computer-aided drug design
12.3 Software tools, web servers, and package
12.4 Promises and challenges of deep learning in computer-aided drug design
12.5 Case studies
12.6 Conclusion
References
Chapter 13: Protein structure prediction with recurrent neural network and convolutional neural network: a case study
Abstract
13.1 Introduction
13.2 Deep learning
13.3 Case studies
13.4 Challenges and limitations for convolutional neural network and recurrent neural network
13.5 Conclusion and outlook
Acknowledgments
Conflict of interest
References
Chapter 14: Generative adversarial networks in protein and ligand structure generation: a case study
Abstract
14.1 Introduction
14.2 Generative adversarial networks: a brief overview
14.3 Machine learning in protein and ligand structure prediction
14.4 Generative modeling for protein and ligand structures
14.5 Generative adversarial networks in protein and ligand structure generation: a case study
14.6 Conclusion
References
Chapter 15: Artificial neural networks for prediction of psychological threats: a case study
Abstract
15.1 Introduction
15.2 Related works
15.3 Insights of the data through visualization
15.4 Proposed work
15.5 Experimentation
15.6 Results and discussion
15.7 Conclusion
References
Index

Khalid Raza

Dr. Khalid Raza is working as an Associate Professor at the Department of Computer Science, Jamia Millia Islamia, New Delhi. Earlier he worked as an “ICCR Chair Professor” at Ain Shams University, Cairo, Egypt. He has many years of teaching & research experiments in the field of Translational Bioinformatics and Computational Intelligence. He has contributed over 120 research articles in reputed Journals and Edited Books. Dr. Raza has authored/edited dozens of books published by reputed publishers. Dr. Raza is an Academic Editor of PeerJ Computer Science International Journal, and Guest Editor of the Journal Natural Product Communications. He has an active collaboration with the scientists from leading institutions of India and abroad. Recently, Dr. Raza has been featured in the list of Top 2% Scientists released by Stanford University (USA) in collaboration with Elsevier. His research interest lies in Machine Learning and its applications in Bioinformatics and Health-informatics.

Affiliations and expertise

Department of Computer Science, Jamia Millia Islamia (Central University), Jamia Nagar, New Delhi, India

Debmalya Barh

Dr. Debmalya Barh is currently a Visiting Full Professor (Titular, Grade-E) in Bioinformatics and Precision Health at the Department of Genetics, Ecology, and Evolution, ICB, Federal University of Minas Gerais, Brazil and honorary scientist of the Institute of Integrative Omics and Applied Biotechnology (IIOAB), India. With over 20 years of experience, he has led academic, healthcare, molecular diagnostic, and bioinformatics industry endeavors. He works with more than 400 scientists from 100+ top ranked organizations across 40+ countries and has 220+ publications and a branded editor for 10+ cutting-edge omics related reference books. He is an expert in in precision/personalized health and integrative omics-based biomarker and target discovery in infectious and complex lifestyle diseases.

Affiliations and expertise

Federal University of Minas Gerais, Belo Horizonte, Brazil

Deepak Singh

Dr. Deepak Singh is an Assistant Professor at the Department of Computer Science and Engineering, National Institute of Technology (NIT) Raipur, India. He has over 10 years of teaching and research experience in various academic institutes. He has published over 30 refereed articles. He served as a reviewer of several journals. He has delivered several invited talks and presented papers in reputed International conferences and workshops. His research interests include evolutionary computation, machine learning, and data mining.

Affiliations and expertise

Department of Computer Science and Engineering, National Institute of Technology, Raipur, Chhattisgarh, India

Naeem Ahmad

Dr. Naeem Ahmad is an Assistant Professor at the Department of Computer Applications, National Institute of Technology (NIT) Raipur, India. Prior to working here, he worked as an Assistant Professor with the School of Network Engineering, Jiangxi Ahead Software Vocational and Technical College. He has over 8 years of teaching/research experience in various academic institutions. He has published over 20 research articles and served as reviewers for various journals and conferences. His research interests lie in deep learning, Wireless Networks, Image Processing, and Signal Processing and its applications in Healthcare.

Affiliations and expertise

Department of Computer Applications, National Institute of Technology, Raipur, Chhattisgarh, India