EAGER: Integrating Multi-Omics Biological Networks and Ontologies for lncRNA Function Annotation using Deep Learning

EAGER：使用深度学习集成多组学生物网络和本体以进行 lncRNA 功能注释

• 批准号: 2400785
• 负责人: Junzhou Huang
• 金额: $ 30万
• 依托单位: University of Texas at Arlington
• 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: 2023-12-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2400785&HistoricalAwards=false
• 关键词: EAGER; Integrating; Multi; Omics; Biological

Long non-coding RNAs (lncRNAs) are a class of ribonucleic acid (RNA) molecules longer than 200 nucleotides that do not encode proteins but play important regulatory roles in various biological and cellular processes such as cancer metastasis, therapeutic targets, immune responses, chromatin remodeling, and embryonic development. Despite exciting findings in recent years, the functions of most lncRNAs remain largely unknown as they are often transcribed from non-coding regions of the genome. Their functions are not always clear and lack conservation across species. The project aims to develop an efficient graph neural network method called Layer-stacked ATTention Embedding to Gene Ontology (LATTE2GO) to reliably annotate lncRNA functions describing each with various gene ontology features including molecular function, biological process, and cellular component. Research activities will engage minorities, women, and undergraduates performing interdisciplinary research through the Girl Engineering Summer Camp, Louis Stokes Alliances for Minority Participation Summer Research Academy, and the McNair programs at the University of Texas at Arlington. The research will aggregate gene ontology structure and multiple interactions between genes, transcripts, and proteins as a knowledge graph containing heterogeneous relationships. The project will (1) extract higher-order multi-omics interrelations from heterogenous interactions as well as multi-relational associations; (2) develop representation learning of lncRNA functions from multiple relationships in the hierarchical gene ontology within the same message-passing framework; and (3) explore attention graph neural networks to effectively aggregate heterogeneous interactions and gene ontology term pertinencies. By extracting higher-order associations and weighting them via attention, LATTE2GO aims to achieve significant gains over previous graph-based function prediction techniques. In addition, the architecture has the advantage of learning features directly from complete hierarchical ontology and connecting with lncRNA network relations in an end-to-end manner. The novel framework could be extended to integrating multiple heterogeneous data sources for generic computational and data science problems.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.

长的非编码RNA（LNCRNA）是一类核糖酸（RNA）分子，超过200个不编码蛋白质但在各种生物学和细胞过程中起重要调节作用的核苷酸分子，例如癌症转移，例如癌症转移，疗法靶标，免疫反应，免疫反应，染色素重塑和胚胎发育。尽管近年来有令人兴奋的发现，但大多数LNCRNA的功能仍然在很大程度上未知，因为它们通常是从基因组的非编码区域转录的。它们的功能并不总是很清楚，并且缺乏整个物种的保护。 该项目旨在开发一种有效的图形神经网络方法，称为“层堆积的注意力嵌入基因本体论”（Latte2Go），以可靠注释lncRNA函数，描述每个功能都具有各种基因本体论特征，包括分子功能，生物学过程和细胞成分。研究活动将与少数民族，妇女和本科生通过女孩工程夏令营进行跨学科研究，路易斯·斯托克斯联盟（Louis Stokes）少数群体参与夏季研究学院以及德克萨斯大学阿灵顿大学的麦克奈尔计划。该研究将汇总基因本体论结构，以及基因，转录本和蛋白质之间的多种相互作用，作为包含异质关系的知识图。该项目将（1）从异源相互作用以及多关系关联中提取高阶多词相互关系； （2）从同一消息填充框架内的分层基因本体中的多个关系中开发出LNCRNA函数的表示。 （3）探索注意力图神经网络有效地汇总了异质相互作用和基因本体论项的相关性。 通过提取高阶关联并通过注意力加权，Latte2Go旨在比以前的基于图的功能预测技术获得显着增长。此外，该体系结构具有直接从完整的层次本体论，并以端到端方式与LNCRNA网络关系建立联系的优势。新颖的框架可以扩展到将多种异构数据源整合到通用计算和数据科学问题上。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审查标准，被认为值得通过评估来获得支持。