Machine learning for clinical trials and precision medicine
Abstract/Contents
- Abstract
- Machine learning (ML) has been wildly applied in biomedicine and healthcare. The growing abundance of medical data and the advance of biological technologies (e.g. next-generation sequencing) have offered great opportunities for using ML in computational biology and health. In this thesis, I present my works contributing to this emerging field in three aspects --- using large-scale datasets to advance medical studies, developing algorithms to solve biological challenges, and building analysis tools for new technologies. In the first part, I present two works of applying ML on large-scale real-world data: one for clinical trial design and one for precision medicine. Overly restrictive eligibility criteria has been a key barrier for clinical trials. In the thesis, I introduce a powerful computational framework, Trial Pathfinder, which enables inclusive criteria and data valuation for clinical trials. A critical goal for precision medicine is to characterize how patients with specific genetic mutations respond to therapies. In the thesis, I present systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinico-genomics data. In the second part, I introduce my work on developing algorithms to solve biological challenge --- aligning multiple datasets with subset correspondence information. In many biological and medical applications, we have multiple related datasets from different sources or domains, and learning efficient computational mappings between these datasets is an important problem. In the thesis, I present an end-to-end optimal transport framework that effectively leverages side information to align datasets. Finally, I present my work on developing analysis tools for new technologies --- spatial transcriptomics and RNA velocity. Recently high-throughput image-based transcriptomic methods were developed and enabled researchers to spatially resolve gene expression variation at the molecular level for the first time. In the thesis, I describe a general analysis tool to quantitatively study the spatial correlations of gene expression in fixed tissue sections. Recent development in inferring RNA velocity from single-cell RNA-seq opens up exciting new vista into developmental lineage and cellular dynamics. In the thesis, I introduce a principled computational framework that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Liu, Ruishan |
|---|---|
| Degree supervisor | Zou, James |
| Thesis advisor | Zou, James |
| Thesis advisor | Soh, H. Tom |
| Thesis advisor | Tse, David |
| Degree committee member | Soh, H. Tom |
| Degree committee member | Tse, David |
| Associated with | Stanford University, Department of Electrical Engineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ruishan Liu. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/yv799ny4496 |
Access conditions
- Copyright
- © 2022 by Ruishan Liu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...