Deep Learning Applications in Translational Bioinformatics

Khalid Raza · Debmalya Barh · Deepak Singh · Naeem Ahmad

2024. g. marts · Elsevier

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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, 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 to foster future research and development in ensemble deep learning and inspire new bioinformatics applications that cannot be attained by using traditional machine learning models. - 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

Par autoru

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.

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.

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.

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.

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