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A Computational Pipeline for Graphical Modeling of Integrated Biomedical Data

Raghu, Vineet (2019) A Computational Pipeline for Graphical Modeling of Integrated Biomedical Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Clinical decision making and biomedical research have the potential to be revolutionized by the abundance of readily available, multi-modal data. One of the main drivers of this wealth of data is next-generation sequencing technologies such as RNA-Seq and Single-cell RNASeq. These methods enable high-throughput measurements of the genome at a granular level. However, to truly understand the causes of disease and the effect of medical interventions, this data must be integrated with phenotypic, environmental, and behavioral data from
individuals. In addition, effective modeling methods that can infer causal relationships from this data are required. This presents a host of modeling challenges such as 1) high dimensionality (low sample size and many variables), 2) redundancy among features, and 3)
unmeasured variables that may confound the system under study. In addition, due to ethical concerns and cost, much of this data is observational. This means that no experimentally controlled perturbation was performed to measure the data. In this thesis, I present a pipeline
to mine causal relationships from this integrated, observational biomedical data. The pipeline
consists of three components: 1) Feature selection and clustering with prior knowledge, 2) Learning undirected graphical model structure to represent the joint distribution of mixed data, and 3) Learning causal graphical models with latent confounding. I demonstrate how this pipeline extracts useful knowledge via two cancer research applications: 1) Prediction of response to a prophylactic cancer vaccine and 2) Early detection of lung cancer from low-dose CT scans.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Raghu, Vineetvineet@cs.pitt.eduvkr80000-0003-3524-3945
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairChrysanthis, Panospanos@cs.pitt.edupanos
Committee CoChairBenos, Panayiotisbenos@pitt.edubenos
Committee MemberHauskrecht, Milosmilos@cs.pitt.edumilos
Committee MemberHwa, Rebeccahwa@cs.pitt.eduhwa
Committee MemberFinn, Oliveraojfinn@pitt.eduojfinn
Committee MemberSpirtes, Peterps7z@andrew.cmu.edu
Date: 30 August 2019
Date Type: Publication
Defense Date: 8 May 2019
Approval Date: 30 August 2019
Submission Date: 6 June 2019
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 151
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Computer Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Graphical Models, Data Exploration, Transcriptomic Analysis, Causal Discovery, Feature Selection, Cancer Genomics
Date Deposited: 30 Aug 2019 15:43
Last Modified: 30 Aug 2019 15:43
URI: http://d-scholarship.pitt.edu/id/eprint/36860

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