framework, parsing genes into an addiction cycle domain, is one way that begins to hint at gene network connectivity and function (see Fig. 1). Big data generating efforts, especially in genomics and the NIH Big Data to Knowledge (BD2K) program, have become an attractive approach for advancing biological discoveries. However, pure big data approaches have been criticized for failing to provide conceptual insights into biological processes (Coveney et al., 2016). It is therefore suggested that big data approaches should rely on theoretical frameworks to avoid circumventing the proven modern scientific method of inquiry and digressing to pre-Baconism methods of radical empiricism (i.e., data generation without reason). The addiction cycle theoretical framework used here, is one theoretical framework, to anchor future genomic studies, rather than fall susceptible to ‘pure blind data gathering,’ which seems to be on the rise. Theoretical frameworks applied to big data (e.g., GWAS) will maximize the production of reliable predictive models, the understanding of the structure and stochastic properties of complex systems, which will ultimately advance actionable conceptual knowledge.
