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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Request a sales quoteDeep 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.
- Addresses the practical application of deep learning algorithms to a wide range of bioinformatics challenges
- Presents integrative and multidisciplinary approaches to ubiquitous healthcare
- Includes case studies to illustrate the concepts discussed
Researchers and graduate students in bioinformatics, health informatics, and machine learning.
- 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
- No. of pages: 298
- Language: English
- Edition: 1
- Volume: 15
- Published: March 7, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443222993
- eBook ISBN: 9780443222986
KR
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.
DB
Debmalya Barh
DS
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.
NA
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.