Big Data Analytics and Information Science for Business and Biomedical Applications: Third Edition
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 3176
Special Issue Editors
Interests: model selection; post-estimation and prediction; shrinkage and empirical Bayes; Bayesian data analysis; machine learning; business; information science; statistical genetics; image analysis
Special Issues, Collections and Topics in MDPI journals
Interests: Bayesian methods; statistical computing; spatial statistics; high-dimensional data; statistical modeling; neuroimaging statistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The world that we live in today is rather data-centric. We encounter data in every walk of life, and the information they contain can be used to improve society, business, health, and medicine. However, making sense of data and extracting meaningful information from it may not be an easy task. In recent years, the rapid growth in the size and scope of datasets in a host of disciplines has created the need for innovative statistical strategies for analyzing and visualizing such data.
An enormous trove of digital data has been produced by biomedicine researchers worldwide, including genetic variants genotyped or sequenced at genome-wide scales, gene expression measured under different experimental conditions, biomedical imaging data such as neuroimaging data, electronic medical records (EMRs) of patients, and many more.
The rise of ‘Big Data’ will not only broaden our understanding of complex human traits and diseases, but will also shed light on disease prevention, diagnosis, and treatment. Undoubtedly, comprehensive analysis of Big Data in genomics and neuroimaging calls for statistically rigorous methods. Various statistical methods have been developed to accommodate the features of genomic studies as well as studies examining the functions and structure of the brain. Simultaneously, statistical theories have also been developed.
Alongside biomedical applications, there has been a growing interest in the use of Big Data in business and financial applications. Financial time series analysis and prediction problems present many challenges for the development of statistical methodology and computational strategies for streaming data.
The analysis of Big Data in biomedical as well as business and financial research has garnered much attention from researchers worldwide. This Special Issue will be the third volume in a series that aims to provide a platform for comprehensive discussions on novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical research, emphasizing varying statistical problems with special emphasis on data analytics and statistical methodology, will be highlighted.
As with the previous two volumes, this Special Issue invites new and original research pertaining to statistical methods and applications in biomedical and business research, including the recent state-of-the-art developments.
Prof. Dr. S. Ejaz Ahmed
Dr. Farouk Nathoo
Guest Editors
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