III: Small: Computational Methods for Multi-dimensional Data Integration to Improve Phenotype Prediction

III：小：多维数据集成的计算方法以改进表型预测

• 批准号: 2246796
• 负责人: Wei Zhang
• 金额: $ 55万
• 依托单位: The University of Central Florida Board of Trustees
• 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: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246796&HistoricalAwards=false
• 关键词: III; Small; Computational; Methods; Multi

Multi-omics is the integration and analysis of multiple types of biological data, including genomics, transcriptomics, proteomics, and epigenomics. By combining these diverse omics data, researchers can gain a comprehensive understanding of biological systems at various molecular levels. However, the integration of data from different omics platforms is challenging due to the varying characteristics and quality of the generated data. Another obstacle is deciphering the complex interactions and regulatory networks across different omics layers, along with understanding their temporal dynamics. Additionally, the interpretability of multi-omics models and the translation of their findings into actionable biological insights remain ongoing challenges for successful phenotype prediction using multi-omics approaches. To tackle these research challenges, this project aims to develop a machine learning-based, multi-dimensional, multi-omics data integration system. This system will extract more accurate molecular signatures for biological interpretation and phenotype prediction. The project's outcomes will reduce barriers in analyzing high-dimensional omics profiles and minimize the time and costs typically associated with biological and biomedical research. Furthermore, the project's dissemination and engagement activities will entice minority students to pursue careers in computer science and bioinformatics.This project focuses on integrating omics data from three dimensions: (1) integrating molecular features generated from RNA-seq data, (2) integrating multi-omics data from different high-throughput sequencing technologies with their regulatory interaction networks, and (3) integrating omics and time-lapse imaging data. The primary objective of this project is to develop comprehensive computational methodologies for addressing critical challenges in molecular signature identification and interpretation using multi-omics platforms. To achieve this goal, the project defines three research thrusts: (1) Transcript variants integration, where a biological pathway-encoded transformer will be developed to integrate transcript variants and mRNA expression from RNA-seq samples to identify biological signatures associated with phenotypes. (2) Multi-omics data integration, involving the development of a generative adversarial network model to predict biological interactions between different biological layers in the multi-omics data and impute missing values in the omics profiles. (3) Imaging and omics integration, which aims to develop a deep learning-based framework to integrate multi-omics profiles and time-lapse microscopy imaging data to enhance phenotype prediction. The integrative machine learning models developed in this project can be applied to various computer science applications for integrating high-dimensional and heterogeneous data sources for sample classification. Additionally, in biological research, this work will facilitate data analytics on large-scale multi-omics profiles and imaging data, leading to improved knowledge interpretation and phenotype prediction compared to current biological measures.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.

多词是对多种类型的生物学数据的整合和分析，包括基因组学，转录组学，蛋白质组学和表观基因组学。通过结合这些多样化的OMIC数据，研究人员可以在各种分子水平上对生物系统有全面的了解。但是，由于生成的数据的特征和质量的不同，来自不同OMIC平台的数据集成变得具有挑战性。另一个障碍是解释不同OMIC层之间的复杂相互作用和调节网络，并了解它们的时间动态。此外，多摩斯模型的解释性及其发现转化为可行的生物学见解仍然是使用多摩学方法成功的表型预测的持续挑战。为了应对这些研究挑战，该项目旨在开发基于机器学习的多维，多摩变数据集成系统。该系统将提取更准确的分子特征，以进行生物学解释和表型预测。该项目的结果将减少分析高维度概况的障碍，并最大程度地减少与生物学和生物医学研究相关的时间和成本。此外，该项目的传播和参与活动将诱使少数群体从事计算机科学和生物信息学领域的职业。本项目的重点是从三个维度整合OMICS数据：（1）将RNA-SEQ数据产生的分子特征集成到RNA-SEQ数据中，（2）将其集成到不同的高度序列效果的数据中，并将其集成到不同的网络中，并将其集成到不同的网络中，并将其整合到不同的网络中，并将其集成到不同的网络中，并将其整合到不同的网络中，并将其整合到不同的网络中，并将其整合到不同的网络中，并将其整合到不同的网络中，并将其集成到不同的网络中。成像数据。该项目的主要目的是开发全面的计算方法，以解决使用多摩西平台的分子签名识别和解释中的关键挑战。为了实现这一目标，该项目定义了三个研究推力：（1）转录本变体集成，其中将开发出生物途径编码的变压器以整合转录物变体和从RNA-Seq样品中的mRNA表达，以识别与表型相关的生物学特征。 （2）涉及生成对抗网络模型的开发，以预测多摩管数据中不同生物学层之间的生物学相互作用，并在OMICS概况中估算缺失值。 （3）成像和OMICS集成，该集成旨在开发一个基于深度学习的框架，以整合多摩管轮廓和延时显微镜成像数据以增强表型预测。该项目中开发的集成机器学习模型可以应用于各种计算机科学应用程序，以集成用于样本分类的高维和异质数据源。此外，在生物学研究中，这项工作将促进有关大规模多派系概况和成像数据的数据分析，与当前的生物学措施相比，可改善知识解释和表型预测。这项奖项反映了NSF的法定任务，并通过评估该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是值得的。