RII Track-2 FEC: Highly Predictive, Explanatory Models to Harness the Life Science Data Revolution

RII Track-2 FEC：利用生命科学数据革命的高度预测性、解释性模型

• 批准号: 2019528
• 负责人: Lauren Shoemaker
• 金额: $ 599.48万
• 依托单位: University of Wyoming
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2019528&HistoricalAwards=false
• 关键词: RII; Track; FEC; Highly; Predictive

Dramatic increases in the scale and availability of data are profoundly reshaping the life sciences. Data acquisition and availability are outpacing our capacity for analysis, including the development of models that represent our knowledge of biological processes. This collaborative project among three universities in Wyoming, Montana, and Nevada will address this pressing need in the life sciences through research and education efforts led by our consortium. Some types of models can fit observed data very well, but lack generality and the ability to extrapolate to novel settings or future time points. Conversely, other types of models can be more general, but provide a poorer fit to individual data sets. In our research we will develop knowledge of these trade-offs and methods that combine advantageous features of different types of models. In each year our consortium will train a diverse cohort of twelve postdoctoral researchers in cutting edge modeling techniques and prepare them for the workforce. The project investigators and postdoctoral researchers at our three institutions will create an integrated, highly collaborative and interdisciplinary consortium of data scientists. We will develop educational tools to aid the dissemination of the methodologies we develop, promoting the efficient use of high dimensional data in the life sciences.Dramatic increases in the scale and availability of data are profoundly reshaping all domains in the life sciences. Data acquisition and availability from DNA sequencers, environmental sensors, parallel global studies, and imagery (among many others), across time and space, are outpacing our capacity for analysis, including the development of models that represent our knowledge of biological processes. We will address this gap in the life sciences through research and education efforts led by our consortium at the University of Wyoming, the University of Montana, and the University of Nevada–Reno. We will compete and further develop computational, statistical, and machine learning methods for multi-dimensional data to develop highly predictive and explanatory models for the life sciences. We will test and refine methods and develop critical tools for harnessing the data revolution. We will apply them to three cross-scale domains within the life sciences and advance our mechanistic understanding of key ecological and evolutionary phenomena. By bringing together a consortium of scientists currently using existing techniques from multiple disciplines, including computer science and applied mathematics, and competing these techniques with simulated and real data, we will evaluate the efficacy of existing methods. Further, we will build on these methods to develop novel hybrid modeling techniques and expanded use of linear and non-linear sparse models to maximize both the predictive accuracy and mechanistic insight of models applied to high dimensional data sets. We will apply these techniques to critical challenges in the life sciences, including mapping phenotypes to genomic data, modeling community dynamics in highly diverse systems, and disentangling the interplay among different temporal scales of drivers in aquatic ecosystems. In each year our consortium will train a diverse cohort of twelve postdoctoral researchers in modeling techniques and prepare them for the workforce. The project investigators and postdoctoral researchers in Wyoming, Montana, and Nevada will create an integrated, highly collaborative and interdisciplinary consortium of data scientists. We will develop educational tools to aid the dissemination of the methodologies we develop, promoting the efficient use of high dimensional data in the life sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据规模的急剧增加和数据可用性正在深刻重塑生命科学。数据采集​​和可用性超过了我们的分析能力，包括开发代表我们对生物过程知识的模型。怀俄明州，蒙大拿州和内华达州的三所大学之间的合作项目将通过我们的财团领导的研究和教育工作来满足生命科学的这种紧迫需求。某些类型的模型可以很好地符合观察到的数据，但是缺乏一般性和推断出新的设置或未来时间点的能力。相反，其他类型的模型可能更笼统，但对单个数据集提供了更差的拟合。在我们的研究中，我们将了解结合不同类型模型的优势特征的这些权衡和方法的知识。每年，我们的财团将培训十二位博士后研究人员的多样性队列，以尖锐的边缘建模技术，并为劳动力做准备。我们三个机构的项目研究人员和博士后研究人员将创建一个综合，高度协作和跨学科的数据科学家联盟。我们将开发教育工具，以帮助传播我们开发的方法，促进生命科学中高维数据的有效利用。数据量表的增加和数据的可用性都深刻地重塑了生命科学中的所有领域。DATA的获取和可用性。包括开发代表我们对生物过程知识的模型。我们将通过我们的联盟，怀俄明大学，蒙大拿大学和内华达大学雷诺大学领导的研究和教育工作来解决生命科学的这一差距。我们将为多维数据竞争并进一步开发计算，统计和机器学习方法，以开发生命科学的高度预测性和利用模型。我们将测试和完善方法，并开发用于利用数据革命的关键工具。我们将将它们应用于生命科学中的三个跨尺度领域，并提高我们对关键生态和进化现象的机械理解。通过汇集一个科学家的联盟，目前使用了包括计算机科学和应用数学在内的多个学科的现有技术，并与模拟和真实数据竞争这些技术，我们将评估现有方法的有效性。此外，我们将基于这些方法来开发新型混合建模技术，并扩展了线性和非线性稀疏模型的使用，以最大程度地提高应用于高维数据集的模型的预测准确性和机械洞察力。我们将将这些技术应用于生命科学的关键挑战，包括将表型映射到基因组数据，对高度多样化系统中的社区动态进行建模，并解散水生生态系统中驱动因素的不同临时尺度之间的相互作用。每年，我们的财团将培训一组不同的在建模技术的博士后研究人员组成的队列，并为劳动力做准备。怀俄明州，蒙大拿州和内华达州的项目调查员和博士后研究人员将创建一个数据科学家的综合，高度协作和跨学科的财团。我们将开发教育工具，以帮助传播我们开发的方法，促进生命科学中高维数据的有效利用。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来评估的审查标准。

项目成果

Increasing temporal variance leads to stable species range limits

增加时间方差导致稳定的物种范围限制

• DOI: 10.1098/rspb.2022.0202
• 发表时间: 2022
• 期刊: Proceedings of the Royal Society B: Biological Sciences
• 作者: Benning, John W.;Hufbauer, Ruth A.;Weiss-Lehman, Christopher
• 通讯作者: Weiss-Lehman, Christopher

Seasonality in Environment and Population Processes Alters Population Spatial Synchrony

• DOI: 10.1086/725804
• 发表时间: 2023-10-01
• 期刊: AMERICAN NATURALIST
• 影响因子: 2.9
• 作者: Walter，Jonathan A.;Reuman，Daniel C.;Shoemaker，Lauren G.
• 通讯作者: Shoemaker，Lauren G.

Disturbance alters transience but nutrients determine equilibria during grassland succession with multiple global change drivers

• DOI: 10.1111/ele.14229
• 发表时间: 2023-05-01
• 期刊: ECOLOGY LETTERS
• 影响因子: 8.8
• 作者: DeSiervo, Melissa H.;Sullivan, Lauren L.;Shoemaker, Lauren G.
• 通讯作者: Shoemaker, Lauren G.

Quantifying mechanisms of coexistence in disease ecology

• DOI: 10.1002/ecy.3819
• 发表时间: 2022-09-29
• 期刊: ECOLOGY
• 影响因子: 4.8
• 作者: Sieben，Andrew J.;Mihaljevic，Joseph R.;Shoemaker，Lauren G.
• 通讯作者: Shoemaker，Lauren G.

Partitioning macroscale and microscale ecological processes using covariate‐driven non‐stationary spatial models

使用协变量驱动的非平稳空间模型划分宏观和微观生态过程

• DOI: 10.1002/eap.2485
• 发表时间: 2022
• 期刊: Ecological Applications
• 影响因子: 5
• 作者: Narr, Charlotte F.;Chernyavskiy, Pavel;Collins, Sarah M.
• 通讯作者: Collins, Sarah M.