Big Data in Biology and Medicine Based on material from a joint workshop with representatives of the international Data-Enabled Life Science Alliance, July 4, 2013, Moscow, Russia

Abstract

FORUM The task of extracting new knowledge from large data sets is designated by the term “Big Data.” To put it simply, the Big Data phenomenon is when the results of your experiments cannot be imported into an Excel file. Estimated, the volume of Twitter chats throughout a year is several orders of magnitude larger than the volume of a person’s memory accumulated during his/her entire life. As compared to Twitter, all the data on human genomes constitute a negligibly small amount [1]. The problem of converting data sets into knowledge brought up by the U.S. National Institutes of Health in 2013 is the primary area of interest of the Data-Enabled Life Science Alliance (DELSA, www.delsaglobal.org) [2]. Why have the issues of computer- aided collection of Big Data created incentives for the formation of the DELSA community, which includes over 80 world-leading researchers focused on the areas of medicine, health care, and applied information science? This new trend was discussed by the participants of the workshop “Convergent Technologies: Big Data in Biology and Medicine.” The total number of workshop participants was 35, including representatives of research institutes dealing with the analysis of large experimental data sets and commercial companies developing information systems. The workshop participants delivered 16 short reports that were aimed at discussing how manipulating large data sets is related to the issues of medicine and health care. The workshop was opened by Prof. Eugene Kolker, who presented a report on the behalf of the Data-Enabled Life Science Alliance (DELSA, www.delsaglobal.org). The alliance supports the globalization of bioinformatics approaches in life sciences and the establishment of scientific communities in the field of “omics.” The main idea is to accelerate translation of the results of biomedical research to satisfy the needs of the community. Large data sets that need to be stored, processed, and analyzed are accumulated in many scientific fields, in addition to biology; there is nothing surprising about this fact.