Kuzmin Konstantin, Lu Xiaoyan, Mukherjee Partha Sarathi, Zhuang Juntao, Gaiteri Chris, Szymanski Boleslaw K
1Network Science and Technology Center, Rensselaer Polytechnic Institute (RPI), 110 Eighth Street, Troy, NY, 12180 USA.
2Rush University Medical Center, Rush University, 1653 W. Congress Parkway, Chicago, IL, 60612 USA.
Appl Netw Sci. 2016;1(1):11. doi: 10.1007/s41109-016-0015-y. Epub 2016 Nov 17.
The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward "safe" incremental research. Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging and it must be supported in competition with established research programs. Therefore, we propose a tool that helps to resolve the tension between safe/fundable research vs. high-risk/potentially transformational research. It does this by identifying hidden overlapping interests around novel molecular research topics. Specifically, it identifies paths of molecular interactions that connect research topics and hypotheses that would not typically be associated, as the basis for scientific collaboration. Because these collaborations are related to the scientists' present trajectory, they are low risk and can be initiated rapidly. Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications. We demonstrate the use of this tool to identify scientists who could contribute to understanding the cellular role of genes with novel associations with Alzheimer's disease, which have not been thoroughly characterized, in part due to the funding emphasis on established research.
包含分子新组合的研究价值在许多创新且屡获殊荣的研究项目中都有所体现。尽管人们呼吁采用创新方法来攻克常见疾病,但越来越多的研究资金流向了“安全”的渐进式研究。通过培育新颖且可能具有变革性的科学研究来对抗这一趋势颇具挑战性,并且必须在与既定研究项目的竞争中获得支持。因此,我们提出一种工具,它有助于化解安全/可资助研究与高风险/潜在变革性研究之间的矛盾。它通过识别围绕新分子研究主题的隐藏重叠兴趣来做到这一点。具体而言,它识别连接通常不会相关联的研究主题和假设的分子相互作用路径,以此作为科学合作的基础。由于这些合作与科学家当前的研究轨迹相关,它们风险较低且可迅速启动。与大多数渐进式步骤不同,这些合作有实现理解上飞跃的潜力,因为它们将研究重新定位用于新的疾病应用。我们展示了该工具的用途,即识别那些能够助力理解与阿尔茨海默病存在新关联、部分因资金侧重于既定研究而尚未得到充分表征的基因的细胞作用的科学家。