Department of Neurological Surgery, University of California San Francisco, San Francisco, California, United States of America.
PLoS One. 2012;7(1):e30463. doi: 10.1371/journal.pone.0030463. Epub 2012 Jan 18.
Information technology (IT) adoption enables biomedical research. Publications are an accepted measure of research output, and network models can describe the collaborative nature of publication. In particular, ecological networks can serve as analogies for publication and technology adoption. We constructed network models of adoption of bioinformatics programming languages and health IT (HIT) from the literature.We selected seven programming languages and four types of HIT. We performed PubMed searches to identify publications since 2001. We calculated summary statistics and analyzed spatiotemporal relationships. Then, we assessed ecological models of specialization, cooperativity, competition, evolution, biodiversity, and stability associated with publications.Adoption of HIT has been variable, while scripting languages have experienced rapid adoption. Hospital systems had the largest HIT research corpus, while Perl had the largest language corpus. Scripting languages represented the largest connected network components. The relationship between edges and nodes was linear, though Bioconductor had more edges than expected and Perl had fewer. Spatiotemporal relationships were weak. Most languages shared a bioinformatics specialization and appeared mutualistic or competitive. HIT specializations varied. Specialization was highest for Bioconductor and radiology systems. Specialization and cooperativity were positively correlated among languages but negatively correlated among HIT. Rates of language evolution were similar. Biodiversity among languages grew in the first half of the decade and stabilized, while diversity among HIT was variable but flat. Compared with publications in 2001, correlation with publications one year later was positive while correlation after ten years was weak and negative.Adoption of new technologies can be unpredictable. Spatiotemporal relationships facilitate adoption but are not sufficient. As with ecosystems, dense, mutualistic, specialized co-habitation is associated with faster growth. There are rapidly changing trends in external technological and macroeconomic influences. We propose that a better understanding of how technologies are adopted can facilitate their development.
信息技术（IT）的采用推动了生物医学研究的发展。出版物是衡量研究产出的公认指标，而网络模型可以描述出版物的协作性质。特别是，生态网络可以作为出版物和技术采用的类比。我们从文献中构建了生物信息学编程语言和健康信息技术（HIT）采用的网络模型。我们选择了七种编程语言和四种类型的 HIT。我们进行了 PubMed 搜索，以确定自 2001 年以来的出版物。我们计算了汇总统计数据并分析了时空关系。然后，我们评估了与出版物相关的专业化、合作、竞争、进化、生物多样性和稳定性的生态模型。HIT 的采用一直不稳定，而脚本语言的采用则迅速增加。医院系统拥有最大的 HIT 研究语料库，而 Perl 拥有最大的语言语料库。脚本语言代表了最大的连通网络组件。边缘和节点之间的关系是线性的，尽管 Bioconductor 的边数比预期的多，而 Perl 的边数比预期的少。时空关系较弱。大多数语言共享生物信息学专业化，表现为互利或竞争关系。HIT 的专业化各不相同。Bioconductor 和放射科系统的专业化程度最高。语言之间的专业化和合作呈正相关，而 HIT 之间则呈负相关。语言进化率相似。语言之间的生物多样性在前十年增长并稳定下来，而 HIT 的多样性则不稳定且平坦。与 2001 年的出版物相比，一年后出版物的相关性为正，而十年后相关性较弱且为负。新技术的采用可能是不可预测的。时空关系有助于采用，但并不充分。与生态系统一样，密集、互利、专业化的共生关系与更快的增长相关。外部技术和宏观经济影响的变化趋势迅速。我们提出，更好地了解技术是如何采用的，可以促进其发展。
