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Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. Usually big data tools perform computation in batch mode and are not optimized for iterative processing...
A system can be defined as an organized, interconnected structure consisting of interrelated and interdependent elements (e.g., components, factors, members, parts). These parts and processes are connected by structural and/or behavioral relationships and continually influence one another directly or indirectly to maintain a balance essential for the existence of the system, and for achieving its...
The goal of Big Data analysis is delineating hidden patterns from data and leverage them into strategies and plans to support informed decision making in a diversity of situations. Big Data are characterized by large volume, high velocity, wide variety, and high value, which may represent difficulties in storage and processing. Research on Big Data repositories has contributed promising results that...
The key to effective cancer treatment is early detection. Risk models built from routinely collected clinical data have the opportunity to improve early detection by identifying high-risk patients. In this study, we explored various machine learning techniques for building a melanoma skin cancer risk model. The dataset contains records of routine dermatology office visits from 9,531,408 patients spread...
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