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- W4298042893 abstract "Nowadays, data analysis is the trend and is used in many applications for different purposes. When data analysis is used in e-healthcare, defence sectors, and the like, it becomes crucial and has more expectations, that is, to provide more accurate results to users/people. Modern biological information has many special characteristics in contrast with data from general application areas, which has received the attention of many research communities to work/analysis for finding efficient (useful) solutions for critical diseases. Data analysis is used for health and clinical informatics, that is, in biomedical applications. Biomedical informatics/biomedical imaging data need to be analyzed properly and effectively with efficient tools, algorithms for 298making (generating) useful decisions. In biomedical informatics, we mainly focus on bioinformatics, imaging, and genetics (BIG). Note that medicine and biomedical sciences are two different fields (for data-context). Usually, biological data are characterized as large quantities, complex structures, high dimensionality, evolving biological concepts, and inadequate data modeling. In fact, there is a thing in common in medicine and biomedical sciences, that is, sophisticated (relevant) data analysis and data mining/learning methods are needed for each field. Biomedical informatics gives a consistent interdisciplinary platform for integrating data and knowledge to provide practical decision-making support in hospitals, health care, and translational science in the analysis of available information. This work provides/presents a detailed overview of bioinformatics aspects of data accumulation and data-driven approaches in medical information science, data and knowledge integration, methodological concerns for data mining model evaluation, transitional bioinformatics, and genetic epidemiology. In summary, this article addresses various useful components such as data characteristics, opportunities, and challenges for statisticians, and recent advances and active areas of bioinformatics, biomedical imaging, and statistical genetics research. These components or topics play an essential role in understanding the concept of biomedical data analysis." @default.
- W4298042893 created "2022-10-01" @default.
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- W4298042893 date "2022-09-30" @default.
- W4298042893 modified "2023-09-30" @default.
- W4298042893 title "Biomedical Data Analysis: Current Status and Future Trends" @default.
- W4298042893 doi "https://doi.org/10.1201/9781003282112-17" @default.
- W4298042893 hasPublicationYear "2022" @default.
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