Aims and Scope
Data analytics is an emerging topic that can be applied to a number of research problems. Recent advances in Artificial Intelligence has produced digital surges in terms of the way digital technologies emerge. The primary aim of data analytics research is to identify useful knowledge from a data and to use that knowledge for a useful application. Machine learning is one such technique that can be applied to several research problems these days due to the tremendous development of digital world.
As we enter into the digital era, massive amount of data is being generated in all domains. In recent days the tremendous amount of data is generated in biological and medical field as the whole digital world is increasing its size due to the development of several data collection methodologies. The increase in the level of data storage systems, in addition to positive consequences, also causes some problems associated with data size growth. Intelligent Systems need data to make smart decisions. Because an effective knowledge base can only be generated using a proper data analytics strategy. Lot of data analysing methodologies were available for handling these data generated. Machine learning is one such methodology that provides a simple way to handle these data.
Bioinformatics is one such field of biology dealing with the storage of tremendous biological data. So data analytics can be done in that bioinformatics data for in-depth analysis. Likewise in the same way, due to the digitization of patient records, analysing the medical data also arise as an emerging field that needs more analysis. Two databases namely bioinformatics and medical informatics are the hottest hub for big data analytics. Extensive data analysis is needed for analysing these databases. Creating a knowledge out of these databases can be used for improving the decision-making strategies.
Machine Learning (ML) is an effective approach for tackling the data analysis part. ML-based methodology for formulating a prediction problem is a hot topic in research these days. ML is considered as a simple, computational efficient and easy to deploy model while comparing with other data handling methodologies. By employing ML in bioinformatics and medical informatics, effective decision process control can be attained. ML can be leveraged to monitor, inform, influence, measure, detect, forecast, advise, reduce, and manage health data.
Lead Guest Editors
Sathishkumar V E
Hanyang University, Republic of Korea
Research Interests: Data Mining, Big data Analytics, Cryptography, Digital Forensics ,Computational Chemistry
Guest Editor
Usha Moorthy
REVA University, India
Research Interests:Big Data Analytics, Data Mining, Medical Data
Special Issue Information
The contributions from researchers describing their original, unpublished, research contribution on the following theme (but not limited to):· Medical data analytics using ML
· Drug design using ML
· Medical image processing using ML
· Recommendation systems using ML
· Protein-Protein interaction analysis using ML
· DNA, RNA, Protein sequence data analysis using ML
· ML for Electronic health records
· ML for biomarks and lifestyle data
· ML for classification and regression problems in bioinformatics
· ML for healthcare applications
· Intelligent systems data analysis
· ML for biological data representation
· ML for big biological data analysis
· ML for interpreting medical data
· ML for smart health monitoring
Manuscript Submission Information
Submission deadline: December 31, 2022
Submissions that pass pre-check will be reviewed by at least two reviewers of the specific field.
Free fast publication and early access will be provided to all accepted papers.
If you have any queries regarding this special issue or other matters, please feel free to contact the editorial office: yu@bonviewpress.org
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ISSN 2811-0854 | Published by Bon View Publishing Pte Ltd.
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