Collaborative Research: RESEARCH-PGR: Predicting Phenotype from Molecular Profiles with Deep Learning: Topological Data Analysis to Address a Grand Challenge in the Plant Sciences

合作研究：RESEARCH-PGR：利用深度学习从分子概况预测表型：拓扑数据分析应对植物科学的重大挑战

• 批准号: 2310357
• 负责人: Arjun Krishnan
• 金额: $ 33.39万
• 依托单位: University of Colorado at Denver
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310357&HistoricalAwards=false
• 关键词: Collaborative; Research; RESEARCH; PGR; Predicting

Organisms are a consequence of information embedded in their genome expressed through molecular processes. Sequencing technologies allow biologists to extract nearly all information content from the genome. However, measuring what an organism is has not advanced as far as genomic sequencing: unlike the genome, it is not yet possible to measure the totality of information embedded in the organismal form. If all the information that is contained within organisms could be extracted, a model could be developed that would address one of the Grand Challenges in biology, the ability to predict what an organism is from its genomic information. In this project, mathematical approaches that have not been fully explored in biology will be used to extract information in data by measuring its structure. This field of mathematics has a motto: that all shape is data, and all data have shape. By measuring the shapes and gene expression patterns of leaves, the project will treat them as data from which embedded information can be extracted. Deep learning methods will then be used to predict the shapes of leaves from their gene expression profiles. As part of the connection between the project and its impact to society, students from both the U.S. and México will help analyze the data through Plants&Python, a bilingual, freely available curriculum initiated as a means to bring together plant biologists who have never coded and data scientists new to plant science, with groups that comprise U.S. agriculture. Using X-ray Computed Tomography (CT) to measure plant morphology and transcriptome profiling (RNA-seq) to measure gene expression, the project will use the Euler Characteristic Transform (ECT) and the Mapper algorithm, two Topological Data Analysis (TDA) techniques, to extract the total information embedded in the leaf morphology of Arabidopsis accessions with contrasting developmental reproducibility. The ECT is mathematically proven to distinguish any object from any other, and the Mapper algorithm is used to visualize underlying data structures as a graph. Specific aims include: 1) using the ECT to measure the total information embedded in leaf shape and benchmarking against traditional methods to see how much “hidden” phenotypic information is revealed when measured comprehensively; 2) generating RNA-Seq gene expression profiles from identical leaves, visualizing the underlying data structure as a Mapper graph; the same will be done for phenotypic data as measured by the ECT; and, 3) predicting the precise leaf shape features associated with gene expression signatures using deep learning. By converting underlying molecular and phenotypic data structures into node embeddings, an encoder-decoder neural network will align molecular and phenotypic Mapper graphs. The result will be a mapping of gene expression profiles to features of leaf shape as predicted using deep learning methods on underlying data structures. All project outcomes will be made publicly available through long term data repositories.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.

生物是通过分子过程表达的基因组中嵌入的信息的结果。测序技术允许生物学家从基因组中提取几乎所有信息含量。但是，在基因组测序方面，测量生物没有采取的效果：与基因组不同，尚不可能测量嵌入有机形式的信息的总数。如果可以提取生物体中包含的所有信息，则可以开发出一种模型，该模型将解决生物学的巨大挑战之一，即从其基因组信息中预测生物体的能力。在这个项目中，生物学尚未完全探索的数学方法将通过测量其结构来提取数据中的信息。这个数学领域具有座右铭：所有形状都是数据，并且所有数据都有形状。通过测量叶子的形状和基因表达模式，该项目将它们视为可以从中提取嵌入信息的数据。然后，深度学习方法将用于预测其基因表达谱的叶子的形状。作为该项目之间的联系及其对社会的影响的一部分，来自美国和墨西哥的学生将通过植物和Python（一种双语，免费的可用课程，作为将从未编码的植物生物学家组合在一起的一种手段，与植物科学的数据科学家一起，与构成美国农业的植物科学的群体一起，来分析数据。使用X射线计算机断层扫描（CT）来测量植物形态和转录组概况（RNA-SEQ）来测量基因表达，该项目将使用Euler特征转换（ECT）和映射算法，两种拓扑数据分析（TDA）技术，将嵌入式信息嵌入了叶片的整体访问中，以解决叶片的介绍。在数学上证明了ECT可以将任何对象与任何对象区分开，并且映射器算法用于将基础数据结构视为图形。具体目的包括：1）使用ECT来测量叶片形状中嵌入的总信息，并根据传统方法进行基准测试，以查看经过全面测量时揭示了多少“隐藏”表型信息； 2）从相同的叶子中生成RNA-Seq基因表达谱，将基础数据结构视为映射图；通过ECT衡量的表型数据也会这样做。 3）预测使用深度学习与基因表达特征相关的精确叶片特征。通过将潜在的分子和表型数据结构转换为节点嵌入，编码器折线神经网络将使分子和表型映射器图对齐。结果将是将基因表达曲线映射到叶片形状的特征，如使用深层学习方法所预测的。所有项目成果将通过长期数据存储库公开获得。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，被认为是通过评估来获得的珍贵支持。