How decision analysis can further nanoinformatics.
How decision analysis can further nanoinformatics.
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DOI：
10.3762/bjnano.6.162
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发表时间：
2015
影响因子：
3.1
通讯作者：
Linkov I
中科院分区：
文献类型：
--
作者：
Bates ME;Larkin S;Keisler JM;Linkov I
纳米材料研究的增加导致了纳米材料数据的增加。下一个挑战是有意义地整合和解释这些数据，以做出更好、更有效的决策。由于纳米材料的复杂性，技术的快速变化，以及不确定性测试和数据发布策略，有关材料特性的信息往往是虚幻的，不确定的，和/或质量不一，这限制了研究人员和监管机构处理和使用数据的能力。纳米信息学的愿景是通过识别支持特定决策所需的信息（自上而下的方法）以及收集和可视化这些相关数据（自下而上的方法）来解决这个问题。然而，目前的纳米信息学工作尚未有效地将数据采集工作集中在与弥合特定纳米材料数据差距最相关的研究上。收集不必要的数据和可视化不相关的信息是昂贵的活动，使决策者不堪重负。我们建议，多准则决策分析（MCDA），信息价值（VOI），证据权重（WOE），和投资组合决策分析（PDA）的决策分析技术可以弥合差距，从目前的数据收集和可视化的努力，目前的信息相关的特定决策需求。决策分析和贝叶斯模型可能是纳米信息学从业者在解决复杂的纳米技术挑战时掌握的机械和统计模型的自然延伸。
The increase in nanomaterial research has resulted in increased nanomaterial data. The next challenge is to meaningfully integrate and interpret these data for better and more efficient decisions. Due to the complex nature of nanomaterials, rapid changes in technology, and disunified testing and data publishing strategies, information regarding material properties is often illusive, uncertain, and/or of varying quality, which limits the ability of researchers and regulatory agencies to process and use the data. The vision of nanoinformatics is to address this problem by identifying the information necessary to support specific decisions (a top-down approach) and collecting and visualizing these relevant data (a bottom-up approach). Current nanoinformatics efforts, however, have yet to efficiently focus data acquisition efforts on the research most relevant for bridging specific nanomaterial data gaps. Collecting unnecessary data and visualizing irrelevant information are expensive activities that overwhelm decision makers. We propose that the decision analytic techniques of multicriteria decision analysis (MCDA), value of information (VOI), weight of evidence (WOE), and portfolio decision analysis (PDA) can bridge the gap from current data collection and visualization efforts to present information relevant to specific decision needs. Decision analytic and Bayesian models could be a natural extension of mechanistic and statistical models for nanoinformatics practitioners to master in solving complex nanotechnology challenges.