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: 1939945
• 负责人: Remco Chang
• 金额: $ 29.95万
• 依托单位: Tufts 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 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1939945&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.

要减轻气候变化对公众健康和环境保护的影响，就需要更好地了解生物过程与环境影响之间的动态相互作用。最先进的技术，这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）方法能够更好地科普非线性问题，并已成功地用于生物学应用，但仍然存在一些障碍，包括算法输出的不透明性和缺乏ML就绪数据。该项目旨在显著推进ML技术，并创建一个新的跨学科领域，即计算生态基因组学。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水平表型性状的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，以在这个关键的地球健康窗口期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开发生物体对环境扰动的反应预测分析，并改变科学家对基因表达和环境的思考方式。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紧急特性的研究所开发概念验证;一个本身将促进许多新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，能够基于有关基因和环境的多尺度数据预测表型。 现有的数据，从简单的矢量到复杂的图像序列，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。 这项研究的核心假设是，深度学习算法和生物知识图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，及时投资于数据科学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，并由HDR和NSF生物科学理事会生物基础设施部门共同支持。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。

项目成果

HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters

• DOI: 10.1111/cgf.14531
• 发表时间: 2021-06
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: G. Appleby;M. Espadoto;Rui Chen;Sam Goree;A. Telea;Erik W. Anderson;Remco Chang

A Problem Space for Designing Visualizations

设计可视化的问题空间

• DOI: 10.1109/mcg.2023.3267213
• 发表时间: 2023
• 期刊: IEEE Computer Graphics and Applications
• 影响因子: 1.8
• 作者: Gleicher, Michael;Riveiro, Maria;von Landesberger, Tatiana;Deussen, Oliver;Chang, Remco;Gillman, Christina
• 通讯作者: Gillman, Christina

Automatic Y-axis Rescaling in Dynamic Visualizations

• DOI: 10.1109/vis49827.2021.9623319
• 发表时间: 2021-09
• 期刊: 2021 IEEE Visualization Conference (VIS)
• 作者: J. Fisher;Remco Chang;Eugene Wu

CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs

CAVA：使用知识图进行探索性柱状数据增强的可视化分析系统

• DOI: 10.1109/tvcg.2020.3030443
• 发表时间: 2021
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Cashman, Dylan;Xu, Shenyu;Das, Subhajit;Heimerl, Florian;Liu, Cong;Humayoun, Shah Rukh;Gleicher, Michael;Endert, Alex;Chang, Remco
• 通讯作者: Chang, Remco

Facilitating Exploration with Interaction Snapshots under High Latency

高延迟下的交互快照促进探索

• DOI: 10.1109/vis47514.2020.00034
• 发表时间: 2021
• 期刊: 2020 IEEE Visualization Conference (VIS
• 作者: Wu, Yifan;Chang, Remco;Hellerstein, Joseph M.;Wu, Eugene
• 通讯作者: Wu, Eugene