Discovering network relations in big time series with application to bioinformatics.

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

Rosario

Simposio; AGRANDA: Simposio de Grandes Datos; 2015

SADIO

Big Data concerns largevolume,complex and growing data sets, with multiple and autonomoussources. It is now rapidly expanding in all science and engineering domains [1]. Time seriesrepresent an important class of big data that can be obtained from several applications, such asmedicine (electrocardiogram), environmental (daily temperature), financial (weekly sales totals, andprices of mutual funds and stocks) [2], as well as from many areas, such as socialnetworksandbiology.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 ofgenes information have become available in the last years, more efficient algorithms for dealingwith such big data in genomics are required [3]. There is an increasing interest in this field for thediscovery of the network of regulations among a group of genes, named Gene Regulation Networks(GRN) [4], by analyzing the genes expression profiles represented as timeseries.