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

长链非编码RNA （Long non-coding RNA, lncRNAs）是一类长度超过200个核苷酸的核糖核酸（RNA）分子，它们不编码蛋白质，但在各种生物和细胞过程中发挥重要的调节作用，如癌症转移、治疗靶点、免疫反应、染色质重塑和胚胎发育。尽管近年来有令人兴奋的发现，但大多数lncrna的功能在很大程度上仍然未知，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明确的，而且在物种间缺乏保护。该项目旨在开发一种高效的图神经网络方法，称为层堆叠关注嵌入基因本体（LATTE2GO），以可靠地注释描述每个lncRNA功能的各种基因本体特征，包括分子功能，生物过程和细胞成分。研究活动将通过女生工程夏令营、路易斯·斯托克斯少数民族参与暑期研究学院联盟和德克萨斯大学阿灵顿分校的麦克奈尔项目，吸引少数民族、妇女和本科生进行跨学科研究。该研究将把基因本体结构和基因、转录本和蛋白质之间的多种相互作用聚合为包含异构关系的知识图谱。该项目将(1)从异质相互作用和多关系关联中提取高阶多组学相互关系；(2)在相同的消息传递框架下，从层次基因本体中的多个关系发展lncRNA功能的表示学习；(3)探索注意图神经网络，有效聚合异构交互和基因本体术语相关性。通过提取高阶关联并通过注意力对它们进行加权，LATTE2GO的目标是比以前基于图的函数预测技术取得显著的进步。此外，该体系结构还具有直接从完整的分层本体中学习特征，并与lncRNA网络关系端到端连接的优势。该框架可以扩展到集成多个异构数据源以解决通用计算和数据科学问题。该奖项反映了美国国家科学基金会的法定使命，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。

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.