Imputation and predictive modeling with biomedical multi-scale data
Abstract/Contents
- Abstract
- In recent years, large amounts of biomedical data are being collected, which allow researchers to discover new knowledge about diseases and to create methods to improve health care. However, when working with the emerging biomedical data, one may encounter problems of incomplete data, small sample data, or multi-scale data, each of which presents a unique challenge for data analysis. In this thesis, we present three pieces of work addressing these challenges. In the first part, we describe a deep learning model for efficient data imputation to handle the problem of incomplete data. When dealing with certain missing-not-at-random cases, an effective and practical shift-correction implementation is developed to improve imputation accuracy. Furthermore, we look into the mechanism that contributes to the new framework's superior performance. In the second part, we describe how we use meta-learning to alleviate the problem of small-sample data in the context of survival prediction. The meta-learning framework shows advantages over competing methods for the case of limited training samples from the target disease. In addition, the model is interpretable and allows us to uncover biological pathways related to the disease outcome. In the final part, we describe different data fusion strategies that integrate data modalities on different scales for improved results in survival prediction. The fusion strategies are evaluated with brain tumor cohorts across different age groups and tumor subtypes. We further examine whether the model learnt from one brain tumor cohort can potentially provide useful information for other cohorts with different disease characteristics.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Qiu, Yeping |
|---|---|
| Degree supervisor | Gevaert, Olivier Michel Simonne |
| Thesis advisor | Gevaert, Olivier Michel Simonne |
| Thesis advisor | Howe, Roger Thomas |
| Thesis advisor | Wootters, Mary |
| Degree committee member | Howe, Roger Thomas |
| Degree committee member | Wootters, Mary |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Yeping Lina Qiu. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Yeping Qiu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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