Senior Design Projects
ECS193 A/B Winter & Spring 2021
Automated Peak Detection and Integration using Machine Learning Algorithms
| qwzzhou@ucdavis.edu | |
| Name | Dave |
| Affiliation | Chemistry |
Project's details
| Project title | Automated Peak Detection and Integration using Machine Learning Algorithms |
| Background | Liquid chromatography – mass spectrometry (LC-MS) is an invaluable tool for many research fields, especially in the biological sciences. Along with the improvements in sample handling and instrumentation throughput, large-scale batch analyses have been commonly employed in clinical research. This generates huge chunk of raw data which needs to be processed and analyzed to get relevant results. One of the major steps in analyzing LC-MS data is the detection of chromatographic peaks and integrating peak areas. There is already some software developed for this, but they usually require the input of several parameters which tend to be batch and instrument specific. It is ideal that peak detection and integration algorithm has minimal user input. One way to implement such is by using machine learning algorithms, specifically where a model can be trained to identify peaks from the chromatogram. Some recent studies have been published with such goal in mind [1,2]. |
| Description | N/A |
| Deliverable | N/A |
| Skill set desirable | Students should have experience with image-based machine learning algorithms. Code should be written in Python. |
| Phone number | 2524121113 |
| Client time availability | 30-60 min weekly or more |
| IP requirement | Client wishes to keep IP of the project |
| Attachment | Click here |
| Selected | N/A |
| Stuff | N/A |
| TA | N/A |