Opportunities and Challenges for the Life Sciences Community

Abstract

Twenty-first century life sciences have transformed into data-enabled (also called data-intensive, data-driven, or big data) sciences. They principally depend on data-, computation-, and instrumentation-intensive approaches to seek comprehensive understanding of complex biological processes and systems (e.g., ecosystems, complex diseases, environmental, and health challenges). Federal agencies including the National Science Foundation (NSF) have played and continue to play an exceptional leadership role by innovatively addressing the challenges of dataenabled life sciences. Yet even more is required not only to keep up with the current developments, but also to proactively enable future research needs. Straightforward access to data, computing, and analysis resources will enable true democratization of research competitions; thus investigators will compete based on the merits and broader impact of their ideas and approaches rather than on the scale of their institutional resources. This is the

Differential Expression Analysis for Pathways

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“…For coping with such complexity, systems biology approaches have proven effective in much of biomedicine, including cancer biology [8,44,60], and these approaches fall into two distinct categories. One consists of techniques for analyzing large "omics" data sets [1,33]. The other category consists of mathematical and computational models for representing underlying processes, as opposed to a specific set of resulting data [4,5,31].…”

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“…Indeed, the sub-discipline of bioinformatics did not exist prior to the establishment of databases such as GenBank and accompanying data repositories. With these informatics tools in hand, one does not strictly need to be a microbiologist to conduct discovery-oriented science in the field of genetics (Kolker, Stewart et al 2012). So equipped, researchers have developed novel insights into the roles of genes in many human diseases (Roy-Engel, Carroll et al 2001).…”