Abstract
In last decades, Bioinformatics has become an emerging field of science with a wide variety of applications in many research areas. The primary goal of bioinformatics is to detect useful biological knowledge hidden under the large volumes of DNA/RNA sequences and structures, literature and other biological and biomedical data, to gain a greater insight into their relationships and, therefore, to enhance the discovery and the comprehension of biological processes. In order to fully exploit the new opportunities that emerge, novel data and text mining techniques have to be developed to effectively address the fundamental biological issue of managing and uncovering meaningful patterns and correlations from these large biological and biomedical data repositories. In this work, we propose an effective data mining technique for analysing biological and biomedical data. The proposed mining process is efficient enough to be applied to various types of biological and biomedical data. To prove the concept, we experiment with applying the data mining technique into two distinct areas, including biomedical text documents and data. In addition, based on the proposed approach, we develop two mining tools, namely the Bio Search Engine and the Genome-Based Population Clustering.
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Available at: ftp://ftp.cs.cornell.edu/pub/smart.
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Ioannou, ZM., Makris, C., Patrinos, G.P. et al. A set of novel mining tools for efficient biological knowledge discovery. Artif Intell Rev 42, 461–478 (2014). https://doi.org/10.1007/s10462-013-9413-z
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DOI: https://doi.org/10.1007/s10462-013-9413-z