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. The applicability of the method has been illustrated by the presented conceptual model of smart digital consultant for personalized breast cancer diagnostics and precision therapy recommendations deploying the suggested method for in silico KDD. The method has been verified and validated for the case studies of differentiated descriptive analytics workflows for prokaryotic and eukaryotic gene finding and mapping and differentiated diagnostics analytics workflows for breast cancer associated gene mutation detection.
Skip Nav Destination
Research Article| December 10 2018
Intelligent method for adaptive in silico knowledge discovery based on big genomic data analytics
AIP Conf. Proc. 2048, 060001 (2018)
Plamenka Borovska, Desislava Ivanova; Intelligent method for adaptive in silico knowledge discovery based on big genomic data analytics. AIP Conf. Proc. 10 December 2018; 2048 (1): 060001. https://doi.org/10.1063/1.5082116
Download citation file: