Autors: Borovska, P. I., Ivanova, D. A.
Title: Intelligent method for adaptive in silico knowledge discovery based on big genomic data analytics
Keywords: In silico Knowledge Discovery, Big Genomic Data Analytics

Abstract: The focus of this paper is on advanced IT and the fourth scientific research paradigm Data Intensive Scientific Discovery (DISD) in support of precision medicine, specifically, for the case study of fighting breast cancer. We suggest intelligent method for adaptive in silico knowledge data discovery based on Big genomic data analytics which is adaptable to important biological, medical and computational aspects. The method is built upon the parallel phase paradigm comprising two overlapping and correlated phases – machine learning phaseand operational phase. The basic functional units in both phases are scientific analytics workflows – bundles of differentiated workflows in the ML phase, and integrated workflow in the operational phase, built upon optimal differentiated workflows stored in the best models and rules repositories. Software system architecture built up on the basis of the method has been proposed.

References

    Issue

    AIP Conference Proceedings, vol. 2048, issue 6000, 2018, United States, https://doi.org/10.1063/1.5082116

    Copyright AIP

    Цитирания (Citation/s):
    1. Software architecture for adaptive in silico knowledge discovery and decision making based on big genomic data analytics - 2019 - в издания, индексирани в Scopus или Web of Science
    2. Conceptual model of integrated approach for in silico knowledge data discovery for breast cancer diagnostics and precision therapy - 2019 - в издания, индексирани в Scopus или Web of Science
    3. Platform for Adaptive Knowledge Discovery and Decision Making Based on Big Genomics Data Analytics - 2019 - в издания, индексирани в Scopus или Web of Science
    4. Sheema, D., Ramesh, K., Data analytics and data mining strategy to improve quality, performance and decision making ( Book Chapter), Data Driven Decision Making using Analytics, pp. 95-110 - 2021 - в издания, индексирани в Scopus или Web of Science

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