权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CAREER: Evaluation of machine learning algorithms for understanding and predicting adaptation to multivariate environments with a Model Validation Program (MVP)

职业：通过模型验证程序 (MVP) 评估机器学习算法，以理解和预测对多变量环境的适应

基本信息

批准号：
2043905
负责人：
Kathleen Lotterhos
金额：
$ 145.99万
依托单位：
Northeastern University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2043905&HistoricalAwards=false
关键词：
CAREER Evaluation machine learning algorithms

项目摘要

Environmental change can be rapid and involve multiple aspects of the environment changing at the same time, such as warming and increased disease pressure. Rapid environmental change threatens the productivity of aquaculture and crops on which humans depend. Predicting organisms' vulnerabilities to rapid and multifactor environmental change, however, is a major scientific challenge. A hurdle to addressing this challenge arises from the complex and non-intuitive ways that organisms adapt, through changes at the level of the DNA sequence, to many environmental stresses at the same time. Thus, there is a need for new approaches to understand and predict adaptation in multivariate environments. To address this need, this project integrates research and education with a Model Validation Program (MVP). The research is developing and evaluating Machine Learning Algorithms (MLAs) for understanding and predicting adaptation of organisms to multivariate environments from their DNA sequences. To evaluate MLAs, this research combines both data simulation and an empirical test in the field with the Eastern Oyster, which provide important ecosystem services and support a multi-million dollar industry. For oysters, this research is studying how temperature, disease pressure, and salinity interact with evolutionary history to determine fitness in the field. This research advances efforts toward addressing the major scientific challenge of predicting adaptation in complex environments by integrating concepts across the frontiers of marine, evolutionary, and statistical sciences in a new way. Machine learning and model validation are not traditionally taught in the marine and environmental sciences, but are becoming increasingly relevant to these fields. As part of a broader education program, this research is developing MVP Learning Modules for high school students and undergraduates, which help students build the foundational knowledge they need to critically evaluate and apply models. Modules are being disseminated to hundreds of students in the greater Boston area and are being made available online for widespread use. The MVP mentoring program is training graduate students, undergraduates, and high school students in marine evolutionary ecology, statistical genomics, and machine learning. This research addresses a pressing societal need to more informatively match genotypes to environments for restoration, farming, and assisted gene flow efforts. Results are being disseminated to stakeholders in the oyster industry.The goal of this research is to evaluate if MLAs, which can model non-linearities, can be used to understand and predict adaptation to multivariate environments under a wide range of scenarios. In Objective 1, the Principal Investigator (PI) is creating simulated datasets with different aspects of realism, and using them to evaluate and refine the MLAs. This novel set of simulations is studying genome evolution under high gene flow in complex, multivariate environments. In Objective 2, the PI is building on their expertise with the Eastern oyster to evaluate the MLAs in a field setting. The PI is first developing a comprehensive seascape genomic dataset and using it to train MLAs to predict an individual's multivariate environment based on a single nucleotide polymorphism genotype. Then, the PI is testing if the MLA prediction can predict the fitness of different genotypes from across the species range when raised in common garden field conditions. In Objective 3, the PI is integrating research and education by using the data obtained from Objs. 1 and 2 to develop a series of original "MVP Learning Modules" with interactive web apps for persons at different levels of understanding, using the relatable example of an oyster restoration project. This research lays the foundation for future studies by producing datasets that could become classical examples for developing and benchmarking innovative modeling approaches.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.

环境变化可能是迅速的，涉及环境的多个方面同时变化，例如变暖和疾病压力增加。快速的环境变化威胁着人类赖以生存的水产养殖和农作物的生产力。然而，预测生物体对快速和多因素环境变化的脆弱性是一项重大的科学挑战。解决这一挑战的一个障碍来自于生物体通过DNA序列水平的变化同时适应许多环境压力的复杂和非直观的方式。因此，需要新的方法来理解和预测在多变量环境中的适应。为了满足这一需求，该项目将研究和教育与模型验证计划（MVP）相结合。该研究正在开发和评估机器学习算法（MLAs），用于从DNA序列中理解和预测生物体对多变量环境的适应。为了评估MLAs，本研究将数据模拟和实地实证测试与东方牡蛎相结合，东方牡蛎提供重要的生态系统服务并支持数百万美元的产业。对于牡蛎，这项研究正在研究温度，疾病压力和盐度如何与进化历史相互作用，以确定该领域的适应性。这项研究通过以一种新的方式整合海洋，进化和统计科学前沿的概念，推动了在复杂环境中预测适应性的重大科学挑战。机器学习和模型验证在海洋和环境科学中传统上没有教授，但与这些领域的关系越来越密切。作为更广泛的教育计划的一部分，这项研究正在为高中生和本科生开发MVP学习模块，帮助学生建立批判性评估和应用模型所需的基础知识。正在向大波士顿地区的数百名学生分发这些单元，并在网上提供以供广泛使用。MVP指导计划正在对研究生、本科生和高中生进行海洋进化生态学、统计基因组学和机器学习方面的培训。这项研究解决了一个紧迫的社会需求，以更多的信息匹配基因型的环境恢复，农业和辅助基因流的努力。研究结果正在向牡蛎产业的利益相关者传播。本研究的目的是评估MLAs是否可以用于理解和预测在各种情景下对多元环境的适应。在目标1中，主要研究者（PI）正在创建具有不同现实主义方面的模拟数据集，并使用它们来评估和改进MLA。这组新的模拟正在复杂的多变量环境中研究高基因流下的基因组进化。在目标2中，PI正在利用他们在东部牡蛎方面的专业知识，在实地环境中评估MLAs。PI首先开发了一个全面的海景基因组数据集，并使用它来训练MLA，以基于单核苷酸多态性基因型预测个体的多变量环境。然后，PI正在测试MLA预测是否可以预测在普通花园田间条件下饲养时不同基因型在整个物种范围内的适合度。在目标3中，PI正在利用从Objs获得的数据将研究和教育结合起来。1和2开发了一系列原创的“MVP学习模块”与互动的网络应用程序的人在不同的理解水平，使用相关的例子牡蛎恢复项目。该研究通过生成数据集为未来的研究奠定了基础，这些数据集可能成为开发和基准测试创新建模方法的经典示例。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。