Collaborative Research: Converging Genomics, Phenomics, and Environments Using Interpretable Machine Learning Models

协作研究：使用可解释的机器学习模型融合基因组学、表型组学和环境

• 批准号: 1940330
• 负责人: Pankaj Jaiswal
• 金额: $ 49.46万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1940330&HistoricalAwards=false
• 关键词: Collaborative; Research; Converging; Genomics; Phenomics

Mitigating the effects of climate change on public health and conservation calls for a better understanding of the dynamic interplay between biological processes and environmental effects. The state-of-the-art, which has led to many important discoveries, utilizes numerical or statistical models for making predictions or performing in silico experimentation, but these techniques struggle to capture the nonlinear response of natural systems. Machine learning (ML) methods are better able to cope with nonlinearity and have been used successfully in biological applications, but several barriers still exist, including the opaque nature of the algorithm output and the absence of ML-ready data. This project seeks to significantly advance technologies in ML and create a new interdisciplinary field, computational ecogenomics. This will be accomplished by designing ML techniques for encoding heterogeneous genomic and environmental data and mapping them to multi-level phenotypic traits, reducing the amount of necessary training data, and then developing interactive visualizations to better interpret ML models and their outputs. These advances will responsibly and transparently inform policy to maximize resources during this crucial window for planetary health, while revealing underlying biological mechanisms of response to stress and evolutionary pressure.The long-term vision for this project is to develop predictive analytics for organismal response to environmental perturbations using innovative data science approaches and change the way scientists think about gene expression and the environment. The goal for this two-year award is to develop a proof-of-concept for an institute focused on predicting emergent properties of complex systems; an institute that would itself foster the development of many new sub-disciplines. The core of this activity is developing a machine learning framework capable of predicting phenotypes based on multi-scale data about genes and environments. Available data, ranging from simple vectors to complex images to sequences, will be ingested into this framework by applying proven semantic data integration tools and algorithmic data transformation methods. The central hypothesis of this research is that deep learning algorithms and biological knowledge graphs will predict phenotypes more accurately across more taxa and more ecosystems than do current numerical and traditional statistical modeling methods. The rationale for this project is that a timely investment in data science will push through a bottleneck in life science, accelerating discovery of gene-phenotype-environment relationships, and catalyzing a new computational discipline to uncover the complex "rules of life."This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity, and is jointly supported by the HDR and the Division of Biological Infrastructure within the NSF Directorate of Directorate for Biological 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.

减轻气候变化对公众健康和自然保护的影响需要更好地了解生物过程和环境影响之间的动态相互作用。最先进的技术，已经导致了许多重要的发现，利用数值或统计模型进行预测或进行硅实验，但这些技术很难捕捉自然系统的非线性响应。机器学习（ML）方法能够更好地处理非线性，并已成功地应用于生物应用，但仍然存在一些障碍，包括算法输出的不透明性质和缺乏ML就绪数据。该项目旨在显著推进机器学习技术，并创建一个新的跨学科领域，计算生态基因组学。这将通过设计用于编码异质基因组和环境数据并将其映射到多层次表型性状的ML技术来实现，减少必要的训练数据量，然后开发交互式可视化以更好地解释ML模型及其输出。这些进展将以负责任和透明的方式为政策提供信息，以便在这个地球健康的关键窗口期间最大限度地利用资源，同时揭示应对压力和进化压力的潜在生物机制。该项目的长期愿景是使用创新的数据科学方法开发对环境扰动的生物体反应的预测分析，并改变科学家对基因表达和环境的看法。这个为期两年的奖项的目标是为一个专注于预测复杂系统的紧急特性的研究所开发一个概念验证；这个研究所本身将促进许多新的分支学科的发展。这项活动的核心是开发一个能够基于基因和环境的多尺度数据预测表型的机器学习框架。可用的数据，从简单的向量到复杂的图像到序列，将通过应用经过验证的语义数据集成工具和算法数据转换方法被摄取到这个框架中。本研究的中心假设是，深度学习算法和生物知识图谱将比当前的数值和传统统计建模方法更准确地预测更多分类群和更多生态系统的表型。该项目的基本原理是，对数据科学的及时投资将突破生命科学的瓶颈，加速发现基因-表型-环境关系，并催化一个新的计算学科来揭示复杂的“生命规则”。该项目是美国国家科学基金会“利用数据革命（HDR）大创意”活动的一部分，由美国国家科学基金会生物科学理事会下属的HDR和生物基础设施部共同支持。该奖项反映了美国国家科学基金会的法定使命，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。