Machine learning assisted biomedical diagnosis and chemical optimizations
Abstract/Contents
- Abstract
- This thesis focuses on applying machine learning methods on the process of biomedical diagnosis and chemical optimizations. The first part describes the combinination of ambient ionization mass spectrometry and machine learning for non-invasive biomedical diagnosis. In this design, mass spectrometry is used to collect chemical information from a biological sample; while machine learning methods are employed to give predictions based on the information. Moreover, the important chemicals in the prediction process can be pin-pointed by feature selection algorithms. Those chemical structures are further identified by tandem mass spectrometry. The second part of this thesis concerns solving chemical optimization problems with reinforcement learning. The processes of optimizing the yield of a chemical reaction and the property of a molecule are formulated as Markov decision processes, and solved by different reinforcement learning algorithms.
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 | Zhou, Zhenpeng |
|---|---|
| Degree supervisor | Zare, Richard N |
| Thesis advisor | Zare, Richard N |
| Thesis advisor | Cegelski, Lynette |
| Thesis advisor | Markland, Thomas E |
| Degree committee member | Cegelski, Lynette |
| Degree committee member | Markland, Thomas E |
| Associated with | Stanford University, Department of Chemistry. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Zhenpeng Zhou. |
|---|---|
| Note | Submitted to the Department of Chemistry. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Zhenpeng Zhou
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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