Text mining systems biology: Turning the microscope back on the observer

Abstract In this review, we describe the relationship between systems biology and text mining. On the one hand, text mining functions as a practical tool for systems biology research, which integrates diverse sub-fields and takes an overall, systems-level view of biological phenomena. In this vein, various analyses have been done to recognize biological entities, construct networks of them (e.g. protein–protein interaction maps) and even find disease-associated genes directly from texts. On the other hand, text mining can also be applied to study systems biology itself, giving a ”distant-reading perspective” on the evolution of the field. For example, by examining changes in the frequencies of terms in systems biology publications, we can analyze trends in research focus and in the popularity of systems approaches in various subdomains (e.g., autism). Given these two uses of text mining for systems biology, we close with suggestions for adapting current publication formats for facilitating text mining and enabling its broader use.