Dataset Information

Temporal patterns of genes in scientific publications.

ABSTRACT: 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.

SUBMITTER: Pfeiffer T

PROVIDER: S-EPMC1924584 | biostudies-literature | 2007 Jul

REPOSITORIES: biostudies-literature

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Publications

Temporal patterns of genes in scientific publications.

Pfeiffer Thomas T Hoffmann Robert R

Proceedings of the National Academy of Sciences of the United States of America 20070709 29

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 fr ...[more]

PMID: 17620606

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Project description:In addition to its primary regulatory role, the Office of Hematology and Oncology Products at the U.S. Food and Drug Administration (FDA) is engaged in many forms of scientific authorship. During the period of 2010 to 2018, FDA oncology staff contributed to 356 publications in the scientific literature. Here, we collaborated with analysts in the Office of Program Planning, Analysis, and Evaluation at the National Institute of General Medical Sciences, National Institutes of Health (NIH), to present a series of analyses aimed at quantifying the characteristics and potential impact of these contributions, as well as characterizing the areas of work addressed. We found that FDA oncology papers are enriched for high-impact publications and have about two times the number of citations as an average NIH-funded paper. Further impact of the publications was measured based on the presence of 65 publications that were cited by guidelines and 12 publications cited by publicly listed clinical trials. The results seen here are promising in determining the impact of FDA oncology publication work but prompt further investigation into longer-term impacts, such as the influence of this work on other regulatory activities at FDA. IMPLICATIONS FOR PRACTICE: This article describes the first comprehensive study of scientific publications produced by U.S. Food and Drug Administration (FDA) oncology staff. The analysis illustrates that staff are highly engaged in publishing in the scientific literature in addition to completing regulatory review work. Publications are generally in clinical medicine, consistent with the large number of medical oncologists working at the Office of Hematology and Oncology Products (OHOP). OHOP publications generally focus either on communicating important regulatory work (approval summaries) or highlighting regulatory science issues to encourage dialogue with the scientific community (commentaries, reviews, and expert working papers). The analysis also suggests that several FDA oncology publications may influence clinical guidelines, but further work is needed to evaluate impact.

Dataset Information

Temporal patterns of genes in scientific publications.

Publications

Temporal patterns of genes in scientific publications.

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