Power-law-like distributions in biomedical publications and research funding

Abstract

Gene annotation, as measured by links to the biomedical literature and funded grants, is governed by a power law, indicating that researchers favor the extensive study of relatively few genes. This emphasizes the need for data-driven science to accomplish genome-wide gene annotation.

Figure 1

Power-law-like distributions. (a) The relationship between the probability P(k) of observing a human gene with k references in Entrez Gene decays according to a power law P(k) ~ k^-a. This trend has also been observed for mouse genes (data not shown) as indexed in the Entrez Gene database. (b) This distribution is also observed when directly searching symbols and aliases in Medline abstracts. The number of genes with zero references is shown in (a) and (b) as black triangles, but were not used in the power-law calculation. (c) Analysis of the CRISP database of NIH-funded grants in 2005 also reveals a power-law relationship. (d) A gamma distribution is most consistent with the research community's goal of genome-wide gene annotation. In this example, gamma-distribution parameters were shape = 2 and scale = 50. Axes are shown in log₁₀ scale.