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Full text release has been delayed at the author's request until August 16, 2025

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Graph Representation Learning for Integrated Analysis of Biological Networks

Li, Mengzhen
http://rave.ohiolink.edu/etdc/view?acc_num=case1720618171884211

Abstract Details

2024, Doctor of Philosophy, Case Western Reserve University, EECS - Computer and Information Sciences.
With the rapid development of biotechnology that generate complex biological data, as well as graph machine learning algorithms to analyze these data, network-based analyses are becoming popular in modern biological research. Many network-based methods have been proposed to process biological data. Network embedding, which learns a representation of nodes into a low-dimensional space, has been a new learning paradigm in the studies of network analysis. Graph Representation learning algorithms are being commonly applied to a broad range of prediction tasks in systems biology. Mapping biological networks into a low-dimensional space enables efective application of machine learning methods in the downstream tasks. In this dissertation we present four new algorithms to compare and integrate biological networks, with a view to improving the reliability of graph machine learning algorithms on biological networks. These algorithms address inter-related problems to provide a comprehensive framework for network integration. Many real-world networks that are used in machine learning have multiple versions that come from diferent sources. For such networks, computation of Consensus Embeddings based on the node embeddings of individual versions can be useful for various reasons. GraphCan is a framework for computing canonical representations for biological networks using a similarity-based Graph Convolutional Network, and it integrates multiple node similarity measures to compute canonical node embeddings for a given network. Consensus embedding is used in our GraphCan model to integrate multiple node similarity measures to compute canonical node embeddings for a given network. GraphCan can be applied to diferent types of downstream tasks. BiGraphCan is an extension of GraphCan, and it aims to make bipartite predictions for understudied proteins using similarity networks and bipartite networks. We also explore network alignment problem by generalizing the Gromov-Wasserstein algorithm. The experiments involve diferent types of biological networks, e.g. protein interaction networks, drug-disease associations and drug interaction networks. We explore diferent types of prediction tasks including link prediction and network alignment. We show that graph representation and integrated analysis improves the efciency and robustness of prediction tasks in biological networks.
Mehmet Koyuturk (Advisor)
Rong Xu (Committee Member)
Yinghui Wu (Committee Member)
Jing Li (Committee Member)
121 p.
Computer Science
Network Embedding, Biological Networks

Recommended Citations

Citations

  • Li, M. (2024). Graph Representation Learning for Integrated Analysis of Biological Networks [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1720618171884211

    APA Style (7th edition)

  • Li, Mengzhen. Graph Representation Learning for Integrated Analysis of Biological Networks. 2024. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1720618171884211.

    MLA Style (8th edition)

  • Li, Mengzhen. "Graph Representation Learning for Integrated Analysis of Biological Networks." Doctoral dissertation, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case1720618171884211

    Chicago Manual of Style (17th edition)

case1720618171884211
© 2024, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.