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

要减轻气候变化对公众健康和保护的影响，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。这项最先进的技术已经导致了许多重要的发现，它利用数字或统计模型进行预测或进行计算机实验，但这些技术难以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问题，并已成功地应用于生物学应用，但仍然存在一些障碍，包括算法输出的不透明性质和缺乏支持ML的数据。该项目旨在显著推进ML的技术，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ML技术来编码不同的基因组和环境数据，并将它们映射到多水平的表型特征，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，从而实现这一点。这些进展将负责任和透明地为政策提供信息，以在这一地球健康的关键窗口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学机制。该项目的长期愿景是使用创新的数据科学方法为生物体对环境扰动的反应开发预测性分析，并改变科学家思考基因表达和环境的方式。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新特性的研究所开发一个概念验证；一个本身将促进许多新的子学科发展的研究所。这项活动的核心是开发一个机器学习框架，能够基于关于基因和环境的多尺度数据预测表型。可用的数据，从简单的矢量到复杂的图像再到序列，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吸收到这个框架中。这项研究的中心假设是，深度学习算法和生物知识图谱将比目前的数值和传统统计建模方法更准确地预测更多类别和更多生态系统的表型。这个项目的基本原理是，对数据科学的及时投资将推动生命科学的瓶颈，加速发现基因-表型-环境关系，并催化一种新的计算学科来揭示复杂的“生命规则”。该项目是国家科学基金会利用数据革命(HDR)大创意活动的一部分，由HDR和NSF生物科学理事会内的生物基础设施部共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.