Temporal patterns of genes in scientific publications.

Publications in scientific journals contain a considerable fraction of our scientific knowledge. Analyzing data from publication databases helps us understand how this knowledge is obtained and how it changes over time. In this study, we present a mathematical model for the temporal dynamics of data on the scientific content of publications. Our data set consists of references to thousands of genes in the >15 million publications listed in PubMed. We show that the observed dynamics may result from a simple process: Researchers predominantly publish on genes that already appear in many publications. This might be a rewarding strategy for researchers, because there is a positive correlation between the frequency of a gene in scientific publications and the journal impact of the publications. By comparing the empirical data with model predictions, we are able to detect unusual publication patterns that often correspond to major achievements in the field. We identify interactions between yeast genes from PubMed and show that the frequency differences of genes in publications lead to a biased picture of the resulting interaction network.