A big data management approach for computer aided breast cancer diagnostic system supporting precision medicine

Abstract

Major challenge in the analysis of clinical data is to propose an integrated and modern access to the progressively increasing amounts of data in multiple formats, and efficient approaches for their management and processing. An approach to management of large amount of heterogeneous data sets from various data sources for a breast cancer diagnostic system is presented in this paper. Big genomic data architecture consists of data sources, storage, integration and preprocessing, real data stream, stream processing, analytical data store, analysis and reporting. Activities at data management for breast cancer diagnostic system are explained. Conceptual database architecture for storing data sets of several types in order to support breast cancer prediction is designed. The breast cancer database comprises of information related to breast cancer genes and functions-id, name, type, organism, function, and proteins coded, description, link for retrieving sequence. The patient's database consists of individual patient data-genetic data, clinical history, individual life style parameters, clinical tests results, environmental factors. The data sets in the suggested big data management system are retrieved from the biomedical research databases. The data management system is platform independent, easy to use and provides access to other databases such PubMed, NCBI. The purpose is to be used for data storage in a system for big data analytics and knowledge discovery, especially for the case study of breast cancer diagnostic. The advantages in data management, analysis, and knowledge discovery empower the scientists to achieve new scientific breakthroughs. As a result the research work is directed towards rapid management and processing of clinical data for solving problems in the field of precision medicine.