Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form
D-Scholarship @ Pitt Banner and Links to Homepage

Graphical models for de novo and pathway-based network prediction over multi-modal high-throughput biological data

Sedgewick, Andrew (2016) Graphical models for de novo and pathway-based network prediction over multi-modal high-throughput biological data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

This is the latest version of this item.

[img]
Preview
 PDF
Download (16MB) | Preview

Abstract

It is now a standard practice in the study of complex disease to perform many high-throughput -omic experiments (genome wide SNP, copy number, mRNA and miRNA expression) on the same set of patient samples. These multi-modal data should allow researchers to form a more complete, systems-level picture of a sample, but this is only possible if they have a suitable model for integrating the data. Due to the variety of data modalities and possible combinations of data, general, flexible integration methods that will be widely applicable in many settings are desirable. In this dissertation I will present my work using graphical models for de novo structure learning of both undirected and directed sparse graphs over a mixture of Gaussian and categorical variables. Using synthetic and biological data I will show that these models are useful for both variable selection and inference. Selecting the regularization parameters is an important challenge for these models so I will also cover stability based methods for efficiently setting these parameters, and for controlling the false discovery rate of edge predictions. I will also show results from a biological application to data from metastatic melanoma patients where our methods identified a PARP1 slice site variant that is predictive of response to chemotherapy. Finally, I present work incorporating miRNA into a pathway based graphical model called PARADIGM. This extension of the model allows us to study patient-specific changes in miRNA induced silencing in cancer.

Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sedgewick, Andrewajsedgewick@gmail.comAJS206
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWasserman, Larry
Thesis AdvisorBenos, Takis
Committee MemberCooper, Greg
Committee MemberVaske, Charles
Committee MemberTawbi, Hussein
Date: 7 September 2016
Date Type: Publication
Defense Date: 29 June 2016
Approval Date: 7 September 2016
Submission Date: 11 August 2016
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 124
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: "Graphical Models", "Pathway Models", cancer, "Machine Learning"
Date Deposited: 07 Sep 2016 13:58
Last Modified: 07 Sep 2017 05:15
URI: http://d-scholarship.pitt.edu/id/eprint/29404

Available Versions of this Item

  • Graphical models for de novo and pathway-based network prediction over multi-modal high-throughput biological data. (deposited 07 Sep 2016 13:58) [Currently Displayed]

Metrics

Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item