Between Dec 19, 2024 and Jan 2, 2025, datasets can be submitted to DRUM but will not be processed until after the break. Staff will not be available to answer email during this period, and will not be able to provide DOIs until after Jan 2. If you are in need of a DOI during this period, consider Dryad or OpenICPSR. Submission responses to the UDC may also be delayed during this time.
 

Network-based learning algorithms for understanding human disease

Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Network-based learning algorithms for understanding human disease

Published Date

2011-03

Publisher

Type

Thesis or Dissertation

Abstract

Advances in genomics, proteomics and molecular pathology with the use of high-throughput technologies, have produced vast datasets identifying thousands of genes whose genomic changes differ in diseased versus normal samples. Many statistical and machine learning methods have been developed to discover biomarkers with potential clinical value, but building reliable learning models for the discovery of biomarkers for prediction of clinical outcomes using high-throughput dataset is still a key challenge in genomic research. This thesis introduces network-based learning algorithms to better utilize large-scale genomic data, and to integrate data with biological prior knowledge to understand the role of genetic changes in human diseases. The first method, NetProp (Network Propagation), is a graph-based semi-supervised feature classification algorithm to identify discriminative biomarkers by learning on bipartite graphs in the analysis of high dimensional genomic data. The second method, HyperPrior, is a hypergraph-based semi-supervised learning algorithm to integrate genomic data with the known biological prior knowledge for biomarker identification and patient's outcome prediction. The third method, MINProp, is a general graph-based learning algorithm to integrate multiple genomic and network data for disease gene discovery. While the method could be applied to discover candidate biomarkers in a high-throughput genomic study, validating the candidate biomarkers is another challenging problem in genomic research. To address this, we introduce a network-based method, rcNet (rank coherence in Network), to elucidate the associations between disease and genes. We applied these methods to large and various real datasets including microarray gene expression profiles, single nucleotide polymorphisms (SNPs), and DNA copy number variations. Our methods identified novel biomarkers with clinical or biological relevance with the disease, as well as achieved competitive classification performance compared with other baseline methods. Our method also successfully validated the associations between diseases and potential disease-causing genes discovered from high-throuput studies. The results indicate that the method that explore the global topological information in the networks, and integrate data with biological prior knowledge could help to discover genetic determinants of human disease, and reveal underlying biological principles of human disease.

Description

University of Minnesota Ph.D. dissertation. March 2011. Major: Computer science. Advisor: Rui Kuang, Ph.D. 1 computer file (PDF)xiv, 99 pages.

Related to

Replaces

License

Collections

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Other identifiers

Suggested citation

Hwang, Tae Hyun. (2011). Network-based learning algorithms for understanding human disease. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/104581.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.