Special Issue: Biomedical data analysis based on deep learning
Guest Editors
Prof. Kelvin K.L. Wong
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Email:kelvin.wong@siat.ac.cn
Prof. Simon Fong
Department of Computer and Information Science, University of Macau.
Email: ccfong@umac.mo
Prof. Dhanjoo N. Ghista
University 2020 Foundation, Northborough, MA, USA.
Email: d.ghista@gmail.com
Manuscript Topics
Biomedicine is an emerging cutting-edge interdisciplinary subject, which is developed by integrating the theories and methods of medicine, life sciences and biology. In recent years, with advanced instruments and equipment and information technology, etc., it has been more and more widely and deeply integrated into biology. In technology, biomedicine generates a large amount of data, and biomedical research involves more and more information technology such as data storage and analysis. The research of biomedical data will receive more and more attention. Reasonable use of analytical methods, collecting and mining available information from massive data, looking for internal connections and laws will bring unprecedented opportunities for biomedical research.
In recent years, deep learning has made breakthrough progress in the fields of natural language processing, computer vision, speech recognition, genomics, and protein structure. Because of the powerful feature extraction capabilities, it is very suitable for processing biomedical data related issues. As a result, diagnosis based on deep learning attracted more and more attention from biomedical researchers. Deep learning technology occupies an increasingly important position in biomedical data analysis due to its profound theoretical value and broad market prospects. With the development of concepts such as artificial intelligence, smart cities, and micro-processing technologies, deep learning in the field of biomedicine technology will play a huge role in both medical and household fields.
This special issue aims to discuss the latest advances in biomedical data analysis based on deep learning technology. Submissions must be unpublished in advance and must not be submitted elsewhere.
Topics of interest include, but are not limited to:
• Disease diagnosis using machine learning
• Medical image processing and intelligent perception
• Medical data modeling based on cluster computing
• Protein structure prediction using high performance computing
• Sequencing data processing in internet of medical things platform
• Expression profiling data analysis using convolutional neural network
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