Discovering network relations in big time series with application to bioinformatics

RUBIOLO, M.; STEGMAYER, G.; MILONE, D.H.

Congreso; AGRANDA - 44 JAIIO; 2015

Big Data concerns large­volume, complex and growing data sets, with multiple and autonomous sources. It is now rapidly expanding in all science and engineering domains. Time series represent an important class of big data that can be obtained from several applications, such as medicine (electrocardiogram), environmental (daily temperature), financial (weekly sales totals, and prices of mutual funds and stocks), as well as from many areas, such as social­networks and biology. Bioinformatics seeks to provide tools and analyses that facilitate understanding of living systems, by analyzing and correlating biological information. In particular, as increasingly large amounts of genes information have become available in the last years, more efficient algorithms for dealing with such big data in genomics are required. There is an increasing interest in this field for the discovery of the network of regulations among a group of genes, named Gene Regulation Networks (GRN), by analyzing the genes expression profiles represented as time­ series.