Full metadata
Title
Statistical Methods for Analysis of Genomic Data with Applications in Oncology
Description
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks.
Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries.
Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
Date Created
2021
Contributors
- Saul, Michelle (Author)
- Dinu, Valentin (Thesis advisor)
- Liu, Li (Committee member)
- Wang, Junwen (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
147 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.161916
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: Ph.D., Arizona State University, 2021
Field of study: Biomedical Informatics
System Created
- 2021-11-16 05:11:22
System Modified
- 2021-11-30 12:51:28
- 3 years ago
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