Network-Centric Data Mining for Medical Applications

Doctoral Dissertation


Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the benefits of data. These methods create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which will in turn generate better data. In order to facilitate the necessary changes, better tools are needed for assessing risk and optimizing treatments, which further require better understanding of disease interdependencies, genetic influence, and translation into a patient’s future. This dissertation explores network-centric data mining approaches for benefit in multiple stages of this feedback loop: from better understanding of disease mechanisms to development of novel clinical tools for personalized and prospective medicine. Applications include predicting personalized patient disease risk based on medical history, optimizing NICU nursing schedules to reduce negative effects, and predicting novel disease-gene interactions.


Attribute NameValues
  • etd-04202012-192658

Author Darcy A. Davis
Advisor Zoltan Toroczkai
Contributor Zoltan Toroczkai, Committee Member
Contributor Nitesh V. Chawla, Committee Member
Contributor Predrag Radivojac, Committee Member
Contributor Scott Emrich, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name Doctor of Philosophy
Defense Date
  • 2011-11-28

Submission Date 2012-04-20
  • United States of America

  • bioinformatics

  • disease gene candidate detection

  • personalized medicine

  • clinical informatics

  • network science

  • translational biology

  • data mining

  • heterogeneous networks

  • link prediction

  • University of Notre Dame

  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units

Digital Object Identifier


This DOI is the best way to cite this doctoral dissertation.


Please Note: You may encounter a delay before a download begins. Large or infrequently accessed files can take several minutes to retrieve from our archival storage system.