Learning based Computational Bioinformatics for Healthcare Big Data

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Guest Editors
Prof. Joel J. P. C. Rodrigues
Federal University of Piauí (UFPI), Brazil; Instituto de Telecomunicações, Portugal
Email: joeljr@ieee.org

Dr. Zhaolong Ning
The University of Hong Kong, Hong Kong
Email: zning@hku.hk

Prof. Yi Guo
Shenzhen People’s Hospital, China
Email: xuanyi_guo@163.com

Dr. Andrea Sciarrone
University of Genoa, Italy
Email: andrea.sciarrone@unige.it

Dr. Petar Solic
University of Split, Croatia
Email: psolic@fesb.hr

Manuscript Topics
The rapid growth of data has brought valuable resources to many areas, and at the same time has brought severe challenges to many fields, such as bioinformatics. The effective use of machine learning technology can discover the hidden rules in big data and tap the potential value of big data, thereby predicting future development, which will greatly promote the overall development of the global economy and society. As a typical technique of machine learning, deep learning employs supervised or unsupervised strategies to automatically learn multi-layer representations of data, and has been successfully applied to the fields of speech recognition, collaborative filtering, and image processing. Although deep learning has made some progress in data feature learning, it still faces many scientific challenges in bioinformatics for healthcare. For example, when the data have the characteristics of inaccuracy, incompleteness, imbalance, etc., the learning results of the algorithms and models will be seriously affected. Therefore, this special issue aims to seek the high-quality papers from academics and industry-related researchers of healthcare big data, computational bioinformatics and machine learning, which conducts research on the shortcomings of learning models for healthcare big data in bioinformatics and present the most recently advanced methods and applications.

Specific topics include but are not limited to:

• Big data theory and methods for computational bioinformatics
• Machine learning for healthcare big data
• Knowledge discovery for healthcare big data
• Domain adaption and transfer learning for healthcare big data
• Computer vision in computational bioinformatics
• Virtual reality in computational bioinformatics
• Learning based neurological disease treatment
• Data analysis in brain networks
• Natural language processing in computational bioinformatics
• Big data analysis and application in other bioinformatics area
• IoT based solutions for healthcare big data
• Security in computational bioinformatics for healthcare big data
• Computational bioinformatics for healthcare big data for COVID-19

Paper Submission
All manuscripts will be peer-reviewed before their acceptance for publication.
The deadline for manuscript submission is November 30th, 2020.

Instructions for authors
http://www.aimspress.com/news/295.html
Please submit your manuscript to online submission system
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