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Deep Learning on Biological Knowledge Graphs

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submitted on 2025-03-16, 05:35 and posted on 2025-03-16, 05:37 authored by Omar Maddouri
Biological data and knowledge bases are increasingly relying on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. Over the last decade, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. In this thesis, we have developed a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate node representations (embeddings) that encode for related information within knowledge graphs. Through the use of symbolic logic, we have shown that these embeddings contain both explicit and implicit information. We have applied these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations. Similarly, we have learned and applied our embeddings to the prediction of disease comorbidities in an additional knowledge graph designed for this purpose and centered on disease instances. Importantly, our approach have demonstrated a performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Interestingly, our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge databases in biology and will expand its usage in machine learning and data analytics.

History

Language

  • English

Publication Year

  • 2017

License statement

© The author. The author has granted HBKU and Qatar Foundation a non-exclusive, worldwide, perpetual, irrevocable, royalty-free license to reproduce, display and distribute the manuscript in whole or in part in any form to be posted in digital or print format and made available to the public at no charge. Unless otherwise specified in the copyright statement or the metadata, all rights are reserved by the copyright holder. For permission to reuse content, please contact the author.

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU

Degree Date

  • 2017

Degree Type

  • Master's

Advisors

Gallouzi, Imed ; Hoehndorf, Robert

Committee Members

Mohamed Abdallah ; Spiridon Bakiras ; Amine Bermak ; Mohammad Karimi ; Byung-Jun Yoon

Department/Program

College of Science and Engineering

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