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

多组学是对多种类型的生物数据的整合和分析，包括基因组学、转录组学、蛋白质组学和表观基因组学。通过结合这些不同的组学数据，研究人员可以在不同的分子水平上全面了解生物系统。然而，由于生成的数据的特征和质量不同，来自不同组学平台的数据集成是具有挑战性的。另一个障碍是破译跨越不同组学层面的复杂相互作用和监管网络，以及理解它们的时间动态。此外，多组学模型的可解释性以及将其发现转化为可操作的生物学见解仍然是使用多组学方法成功进行表型预测的持续挑战。为了应对这些研究挑战，本项目旨在开发一个基于机器学习的、多维、多组学的数据集成系统。该系统将为生物学解释和表型预测提取更准确的分子特征。该项目的成果将减少分析高维组学资料的障碍，并将通常与生物和生物医学研究相关的时间和成本降至最低。此外，该项目的传播和参与活动将吸引少数族裔学生投身计算机科学和生物信息领域。该项目侧重于从三个维度整合组学数据：(1)整合由RNA-SEQ数据产生的分子特征；(2)将来自不同高通量测序技术的多组学数据与其调控相互作用网络整合；(3)整合组学和延时成像数据。该项目的主要目标是开发全面的计算方法，利用多组学平台解决分子签名识别和解释方面的关键挑战。为了实现这一目标，该项目确定了三个研究重点：(1)转录变体整合，其中将开发一个生物途径编码的转换器，以整合来自RNA-SEQ样本的转录变体和mRNA表达，以确定与表型相关的生物特征。(2)多组学数据集成，包括开发一个生成性对抗网络模型来预测多组学数据中不同生物层之间的生物相互作用，并归因于组学数据中的缺失值。(3)成像和组学集成，旨在开发一个基于深度学习的框架，集成多个组学数据和时间推移显微成像数据，以增强表型预测。本项目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，以集成高维和异质数据源进行样本分类。此外，在生物研究方面，这项工作将促进对大规模多组学概况和成像数据的数据分析，导致与当前的生物测量相比，改进的知识解释和表型预测。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。

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.