Toward faster and more data-efficient computational biology
Abstract/Contents
- Abstract
- Limited computing power and limited sample size are two central challenges in computational biology. While the next-generation sequencing technology offers a highly scalable way for measuring genomic information, the size of the data for a single biological sample can be as large as tens of gigabytes and presents a tremendous challenge for data storage and processing. At the same time, the data is usually ultra-high-dimensional, e.g., tens of thousands of genes or millions of mutations, requiring a large number of samples for effective inference. In this thesis, I address these two challenges by designing fundamentally better algorithms for several key applications. First, we address the limited computing power problem by presenting two works of algorithm acceleration using a strategy that we call adaptive Monte Carlo computation. Such a strategy first converts the deterministic computational problem into a statistical estimation problem and then accelerates the process by adaptive sampling. Then we move on to the limited sample size problem and consider two aspects, i.e., optimizing the experimental design and borrowing information from other datasets. For the former, we present work on the optimal experimental design for single-cell RNA-Seq. For the latter, we consider multiple hypothesis testing using side information and dimensionality reduction guided by additional datasets.
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 | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhang, Jinye |
|---|---|
| Degree supervisor | Tse, David |
| Thesis advisor | Tse, David |
| Thesis advisor | Montanari, Andrea |
| Thesis advisor | Zou, James |
| Degree committee member | Montanari, Andrea |
| Degree committee member | Zou, James |
| Associated with | Stanford University, Department of Electrical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Martin Jinye Zhang. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Jinye Zhang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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