Modeling protein sequences, structures and functions with deep neural networks

Liu, Yang

Permalink

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

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

Owning Collections