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Báo cáo y học: " Power-law-like distributions in biomedical publications and research funding"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Power-law-like distributions in biomedical publications and research funding. | Correspondence Power-law-like distributions in biomedical publications and research funding Andrew I Su and John B Hogenesch Addresses Genomic Institute of the Novartis Research Foundation 10675 John Jay Hopkins Drive San Diego CA 92121 USA. Department of Pharmacology Institute for Translational Medicine and Therapeutics University of Pennsylvania School of Medicine 421 Curie Blvd Philadelphia PA 19104 USA. Correspondence John B Hogenesch. Email hogenesc@mail.med.upenn.edu Published 30 April 2007 Genome Biology 2007 8 404 doi 10.1186 gb-2007-8-4-404 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2007 8 4 404 2007 BioMed Central Ltd 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. Following the completion of the primary sequence of the mouse and human genomes one of the key challenges for the biomedical community is the functional annotation of all genes 1 . With more than 650 000 citations indexed in Medline in 2005 alone it is tempting to assume that our understanding of gene function is steadily and uniformly progressing. As one method of quantifying our progress toward this ambitious goal of genome-wide gene annotation we analyzed links into the biomedical literature as curated and indexed in the Entrez Gene database of the National Center for Biotechnology Information NCBI 2 . At the time of our study there were 40 822 human genes in the database. We observe that the probability P k that a gene has k references decays by a power law P k k-a a 1.31 Figure 1a . Simply put over all human genes in the Entrez Gene database the most common number of linked citations is zero 16 346 entries not used in calculation the next most common is one linked citation 6 325 genes .