A holistic approach to testing biomedical hypotheses and analysis of biomedical data

Abstract

Testing biomedical hypotheses is performed based on advanced and usually many-step analysis of biomedical data. This requires sophisticated analytical methods and data structures that allow to store intermediate results, which are needed in the subsequent steps. However, biomedical data, especially reference data, often change in time and new analytical methods are created every year. This causes the necessity to repeat the iterative analyses with new methods and new reference data sets, which in turn causes frequent changes of the underlying data structures. Such instability of data structures can be mitigated by the use of the idea of data lake, instead of traditional database systems. The aim of this paper is to show system for researchers dealing with various types of biomedical data. Such a system provides a functionality of data analysis and testing different biomedical hypotheses. We treat a problem in a holistic way giving a researcher freedom in configuration his own multi-step analysis. This is possible by using a multiversion dynamic-schema data warehouse, performing parallel calculations on the virtualized computational environment, and delivering data in MapReduce-based ETL processes.