University of Illinois Urbana-Champaign

Modeling protein sequences, structures and functions with deep neural networks

Liu, Yang

Content Files
LIU-DISSERTATION-2023.pdf

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https://hdl.handle.net/2142/121527

Description

Title
Modeling protein sequences, structures and functions with deep neural networks
Author(s)
Liu, Yang
Issue Date
2023-07-14
Director of Research (if dissertation) or Advisor (if thesis)
Peng, Jian
Doctoral Committee Chair(s)
Peng, Jian
Committee Member(s)
  • Han, Jiawei
  • Zhai, Chengxiang
  • Liu, Qiang
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • machine learning
  • deep learning
  • artificial intelligence
  • computational biology
  • bioinformatics
Abstract
In the rapid-advancing field of biotechnology, proteins - the fundamental building blocks of life - play a critical role in addressing an array of complex biological challenges. As the cost of experimentation is extremely high and various biological datasets have been created, computational methods for understanding proteins have become essential. In this dissertation, we introduced several machine learning algorithms aiming at improving protein structure and function modeling by leveraging data-driven principles. First, we introduce a deep learning approach for protein contact prediction which uses a deep convolutional network to learn meaningful structural motifs based on experimental data. Second, we detail a data-driven method for learning protein structural representation enabling both high-performance and high-efficiency structural searches. Third, we introduce an end-to-end protein network alignment learning algorithm that integrates heterogeneous information from biological network using graph neural networks. In summary, these developments have demonstrated the potential of applying data-driven principle through novel machine learning algorithms to address the challenges in protein modeling, yielding learned biological insights.
Graduation Semester
2023-08
Type of Resource
Thesis
Handle URL
https://hdl.handle.net/2142/121527
Copyright and License Information
Copyright 2023 Yang Liu

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