Machine learning methodologies for high dimensional biomedical & bioinformatics applications

Li, Yutong

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https://hdl.handle.net/2142/110696 Copy

Description

Title

Machine learning methodologies for high dimensional biomedical & bioinformatics applications

Author(s)

Li, Yutong

Issue Date

2021-04-21

Director of Research (if dissertation) or Advisor (if thesis)

Zhu, Ruoqing

Doctoral Committee Chair(s)

Zhu, Ruoqing

Committee Member(s)

• Qu, Annie
• Zhao, Dave Sihai
• Li, Bo

Department of Study

Statistics

Discipline

Statistics

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Machine Learning
• Matrix Decomposition
• Clustering
• Deep Learning
• Biomedical Applications
• Bioinformatics

Abstract

The impact of machine learning has been greatly expanded due to the increase in computational power in recent years, and has made a significant scientific contribution to many fields. This dissertation primarily investigates and expands the usage of certain machine learning methodologies on high-dimensional biomedical and bioinformatics applications. In particular, I aim to propose novel, data-driven clustering and feature extraction methods to uncover richer and more interpretable predictive features for classification problems. This dissertation considers three modern biomedical and bioinformatics problems in the context of text mining, computer vision and microbiome analysis. To address the different challenges in these applications, novels methods in matrix factorization, image registration, and deep learning are proposed. For the first project on text mining, we propose the semi-orthogonal non-negative matrix factorization as a topic model to investigate the potential of using triage notes to classify patient disposition in addressing the issue of emergency department crowding. For the second project on computer vision, we propose a novel implementation of the neural style transfer algorithm as an image preprocessing and registration method for skin lesion classification problems. For the third project on microbiome analysis, we discuss two works that have been done. First, we propose an analysis pipeline that implements the random forest model to identify food intake, along with a PCA-based approach to remove study batch effects and validate our classification results. Second, we proposed to incorporate the phylogenetic information of microbes as graphs via a graphical convolutional neural network to improve the classification performances for dietary and health outcomes.

Graduation Semester

2021-05

Type of Resource

Thesis

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http://hdl.handle.net/2142/110696

Copyright and License Information

Copyright 2021 by Yutong Li. All rights reserved.

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