Autors: Ivanova, D. A., Borovska, P. I.
Title: Scalable framework for adaptive in-silico knowledge discovery and decision-making out of genomic big data
Keywords: Scalable framework, in-silico knowledge discovery, decision-making, genomic big data

Abstract: This paper presents the concept and the modern advances of big data analytics and its influence in the area of genomics for adaptive in-silico knowledge discovery and decision-making with respect to precision and personalized medicine. The goal of the paper is to build up the scalable framework, providing a set of software tools for applying the methods in research and experimental activities for precision medicine support, establishing a modern research infrastructure that will allow for significant scientific outcomes, development of new methods and algorithms to manage big data streams, deployment of new streaming and parallel processing technologies of large sets of scientific data obtained from experiments. The scalability of the working framework reduces computational time and support optimization by involving resource reconfiguration and parallel processing. The proposed scalable framework is verified for the case studies of Multiple Sequence Alignment (MSA) based on social beh



    AMEE, vol. 2048, pp. 060019-1 - 060019-7, 2018, United States, AIP, ISBN 978-073541774-8

    Copyright AIP

    Вид: публикация в международен форум, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science