University of Illinois Urbana-Champaign

Machine learning for large and small data biomedical discovery

Luo, Yunan

Content Files
LUO-DISSERTATION-2021.pdf

Permalink

https://hdl.handle.net/2142/113873

Description

Title
Machine learning for large and small data biomedical discovery
Author(s)
Luo, Yunan
Issue Date
2021-11-30
Director of Research (if dissertation) or Advisor (if thesis)
Peng, Jian
Doctoral Committee Chair(s)
Peng, Jian
Committee Member(s)
  • El-Kebir, Mohammed
  • Han, Jiawei
  • Ma, Jianzhu
  • Cho, Hyunghoon
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2022-04-29T21:34:36Z
Keyword(s)
  • computational biology
  • bioinformatics
  • machine learning
  • deep learning
  • artificial intelligence
Abstract
In modern biomedicine, the role of computation becomes more crucial in light of the ever-increasing growth of biological data, which requires effective computational methods to integrate them in a meaningful way and unveil previously undiscovered biological insights. In this dissertation, we introduce a series of machine learning algorithms for biomedical discovery. Focused on protein functions in the context of system biology, these machine learning algorithms learn representations of protein sequences, structures, and networks in both the small- and large-data scenarios. First, we present a deep learning model that learns evolutionary contexts integrated representations of protein sequence and assists to discover protein variants with enhanced functions in protein engineering. Second, we describe a geometric deep learning model that learns representations of protein and compound structures to inform the prediction of protein-compound binding affinity. Third, we introduce a machine learning algorithm to integrate heterogeneous networks by learning compact network representations and to achieve drug repurposing by predicting novel drug-target interaction. We also present new scientific discoveries enabled by these machine learning algorithms. Taken together, this dissertation demonstrates the potential of machine learning to address the small- and large-data challenges of biomedical data and transform data into actionable insights and new discoveries.
Graduation Semester
2021-12
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/113873
Copyright and License Information
Copyright 2021 Yunan Luo

Owning Collections

Graduate Dissertations and Theses at Illinois PRIMARY
Graduate Theses and Dissertations at Illinois