Keserci Samet, Livingston Eric, Wan Lingtian, Pico Alexander R, Chacko George
NETE Labs, NET ESolutions Corporation, McLean, VA, USA.
Research Intelligence, Elsevier Inc., New York, NY, USA.
Heliyon. 2017 Nov 15;3(11):e00442. doi: 10.1016/j.heliyon.2017.e00442. eCollection 2017 Nov.
Drug discovery and subsequent availability of a new breakthrough therapeutic or 'cure' is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To document and understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. Building upon prior art, we have conducted a case study of five anti-cancer therapeutics to identify the collaborations that resulted in the successful development of these therapeutics both within and across their respective networks. We have linked the work of over 235,000 authors in roughly 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks. We have enriched the content of these networks by annotating them with information on research awards from the US National Institutes of Health (NIH). Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.
药物发现以及随后新的突破性治疗方法或“治愈方案”的出现,是研究进展给社会带来益处的一个有力例证。这些进展无一例外都是合作的成果,涉及众多科学家对一个发现网络的贡献,而理论和实验正是在此基础上构建起来的。为了记录和理解此类科学进展,利用公共和商业数据源的数据挖掘并结合网络分析,可以作为一种数字化方法,用以汇总和分析一种治疗方法发展历程中的各个组成事件。这种方法不仅适用于治疗方法的发展历程,更广泛地说,它有助于(i)提高探索研究进展科学历史的效率;(ii)记录和理解合作情况;(iii)进行投资组合分析、规划和优化;(iv)传达研究的社会价值。基于现有技术,我们对五种抗癌治疗方法进行了案例研究,以确定在各自网络内部及跨网络促成这些治疗方法成功研发的合作关系。我们将大约106,000篇科学出版物中超过235,000名作者的工作联系起来,这些出版物涵盖了这五种治疗方法研发所需的关键研究。通过应用回顾性引文发现,我们确定了所有五种治疗方法网络中都被引用的一组核心出版物,以及网络组合中的其他交叉点。我们通过用美国国立卫生研究院(NIH)研究奖项的信息对这些网络进行注释,丰富了它们的内容。最后,我们将这些奖项与其对应的同行评审小组进行了映射,识别出了影响这些网络中所代表研究的另一层合作性科学活动。