Large-scale correlation mining for biomolecular network discovery

Abstract

Continuing advances in high-throughput mRNA probing, gene sequencing and microscopic imaging technology is producing a wealth of biomarker data on many different living organisms and conditions. Scientists hope that increasing amounts of relevant data will eventually lead to better understanding of the network of interactions between the thousands of molecules that regulate these organisms. Thus progress in understanding the biological science has become increasingly dependent on progress in understanding the data science. Data mining tools have been of particular relevance since they can sometimes be used to effectively separate the “wheat” from the “chaff”, winnowing the massive amount of data down to a few important data dimensions. Correlation mining is a data mining tool that is particularly useful for probing statistical correlations between biomarkers and recovering properties of their correlation networks. However, since the number of correlations between biomarkers is quadratically larger than the number biomarkers, the scalability of correlation mining in the big data setting becomes an issue. Furthermore, there are phase transitions that govern the correlation mining discoveries that must be understood in order for these discoveries to be reliable and of high confidence. This is especially important to understand at big data scales where the number of samples is fixed and the number of biomarkers becomes unbounded, a sampling regime referred to as the ”purely-high dimensional setting.” In this chapter, we will discuss some of the main advances and challenges in correlation mining in the context of large scale biomolecular networks with a focus on medicine. A new correlation mining application will be introduced: discovery of correlation sign flips between edges in a pair of correlation or partial correlation networks. The pair of networks could respectively correspond to a disease (or treatment) group and a control group. This paper is to appear as a chapter in the book Big Data over Networks from Cambridge University Press (ISBN: 9781107099005). 4 Large scale correlation mining for biomolecular network discovery