University of Illinois Urbana-Champaign

Entropy-based machine learning algorithms applied to genomics and pattern recognition

Moon, Wooyoung

Content Files
MOON-DISSERTATION-2019.pdf

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https://hdl.handle.net/2142/104838

Description

Title
Entropy-based machine learning algorithms applied to genomics and pattern recognition
Author(s)
Moon, Wooyoung
Issue Date
2019-04-16
Director of Research (if dissertation) or Advisor (if thesis)
Song, Jun S.
Doctoral Committee Chair(s)
Dahmen, Karin
Committee Member(s)
  • Kuehn, Seppe
  • Draper, Patrick
Department of Study
Physics
Discipline
Physics
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2019-08-23T19:51:55Z
Keyword(s)
Machine Learning, Decision Trees, Convolutional Filters, Genomics, Cancer, Entropy
Abstract
Transcription factors (TF) are proteins that interact with DNA to regulate the transcription of DNA to RNA and play key roles in both healthy and cancerous cells. Thus, gaining a deeper understanding of the biological factors underlying transcription factor (TF) binding specificity is important for understanding the mechanism of oncogenesis. As large, biological datasets become more readily available, machine learning (ML) algorithms have proven to make up an important and useful set of tools for cancer researchers. However, there remain many areas for potential improvements for these ML models, including a higher degree of model interpretability and overall accuracy. In this thesis, we present decision tree (DT) methods applied to DNA sequence analysis that result in highly interpretable and accurate predictions. We propose a boosted decision tree (BDT) model using the binary counts of important DNA motifs to predict the binding specificity of TFs belonging to the same protein family of binding similar DNA sequences. We then proceed to introduce a novel application of Convolutional Decision Trees (CDT) and demonstrate that this approach has distinct advantages over the BDT modeil while still accurately predicting the binding specificty of TFs. The CDT models are trained using the Cross Entropy (CE) optimization method, a Monte Carlo optimization method based on concepts from information theory related to statistical mechanics. We then further study the CDT model as a general pattern recognition and transfer learning technique and demonstrate that this approach can learn translationally invariant patterns that lead to high classification accuracy while remaining more interpretable and learning higher quality convolutional filters compared to convolutional neural networks (CNN).
Graduation Semester
2019-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/104838
Copyright and License Information
Copyright 2019 Wooyoung Moon

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