Autors: Gancheva, V. S., Georgiev, I.
Title: Software architecture for adaptive in silico knowledge discovery and decision making based on big genomic data analytics (Open Access)
Keywords: -

Abstract: During the last years leading scientists, researchers and analysts determine big data as revolution in scientific studies and one of the most challenging tendencies. The volume of stored genomic data has increased significantly. Main challenge in data analysis and knowledge discovery is to suggest efficient processing tools, methods and technologies. Software architecture for adaptive knowledge discovery based on big genomic data analytics is presented in this paper. The software architecture is comprised of layers for data integration and preprocessing, database/data warehouse server, data discovery engine, pattern evaluation and graphical user interface. The big genomic data architecture consists of data sources, storage, integration and preprocessing, real data stream, stream processing, analytical data store, analysis and reporting. An algorithm for prediction of breast cancer based on machine learning for analysis of big genomic data is presented.

References

    Issue

    AIP Conference Proceedings., vol. 2172, pp. Article number 090009, 2019, United States, American Institute of Physics Inc., DOI 10.1063/1.5133586

    Copyright AIP Conference Proceedings

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    Вид: пленарен доклад в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus