Skip to main content
eScholarship
Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UCLA Electronic Theses and Dissertations (2022)

Large-scale Knowledge Graphs (KGs), such as Wikipedia and many enterprises or other domain-specific KGs, contain large numbers of real-world facts and are ubiquitous and foundational to many downstream knowledge-driven AI applications. Many existing techniques have applied state-of-the-art machine learning (ML) techniques in knowledge graph modeling to improve the performance in these applications with the KG backend, but the semantic structures especially the hierarchical ontological information inside the KGs are sparsely investigated and therefore relatively less leveraged into KG learning.

In this dissertation, we demonstrate a series of research results that systematically explores how such hierarchical ontological components in knowledge graphs are incorporated into KG representation learning. We present multiple practical machine learning methods, such as hierarchical graph modeling, graph neural networks, self-supervised learning, and language models, that can effectively and efficiently capture ontological information, given different knowledge graph formulations. As a result, our proposed approaches address various real-world challenges in multiple domains, from knowledge graph itself, to diverse disciplines including natural language processing, recommender system, bioinformatics, and societal studies, and expand ML frontiers to knowledge graphs.

    Cover page: Incorporating Ontological Information in Knowledge Graph Learning and Empowered Interdisciplinary Applications
    • Article
    • Peer Reviewed
    UCLA Previously Published Works (2021)

    Motivation

    Circular RNA (circRNA) is a novel class of long non-coding RNAs that have been broadly discovered in the eukaryotic transcriptome. The circular structure arises from a non-canonical splicing process, where the donor site backspliced to an upstream acceptor site. These circRNA sequences are conserved across species. More importantly, rising evidence suggests their vital roles in gene regulation and association with diseases. As the fundamental effort toward elucidating their functions and mechanisms, several computational methods have been proposed to predict the circular structure from the primary sequence. Recently, advanced computational methods leverage deep learning to capture the relevant patterns from RNA sequences and model their interactions to facilitate the prediction. However, these methods fail to fully explore positional information of splice junctions and their deep interaction.

    Results

    We present a robust end-to-end framework, Junction Encoder with Deep Interaction (JEDI), for circRNA prediction using only nucleotide sequences. JEDI first leverages the attention mechanism to encode each junction site based on deep bidirectional recurrent neural networks and then presents the novel cross-attention layer to model deep interaction among these sites for backsplicing. Finally, JEDI can not only predict circRNAs but also interpret relationships among splice sites to discover backsplicing hotspots within a gene region. Experiments demonstrate JEDI significantly outperforms state-of-the-art approaches in circRNA prediction on both isoform level and gene level. Moreover, JEDI also shows promising results on zero-shot backsplicing discovery, where none of the existing approaches can achieve.

    Availability and implementation

    The implementation of our framework is available at https://github.com/hallogameboy/JEDI.

    Supplementary information

    Supplementary data are available at Bioinformatics online.
      Cover page: JEDI: circular RNA prediction based on junction encoders and deep interaction among splice sites
      Top