Student Work
Exploring Machine Learning Methods for Nuclear Export Sequence Identification
PublicThe goal of this project is to design and implement a user-friendly machine learning tool that can be applied to the classification of polypeptides to find functional nuclear export sequences (NESs). This tool incorporates an API that takes advantage of support vector machines and can be expanded to include other models. Because NESs have been found to have consistent structure, structural data is incorporated into the model to increase confidence. This report is accompanied by a manual that instructs users on how to use the tool.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
- Creator
- Publisher
- Identifier
- E-project-051520-181059
- Advisor
- Year
- 2020
- Date created
- 2020-05-15
- Resource type
- Major
- Rights statement
- License
Relations
- In Collection:
Items
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Exploring_Machine_Learning_Methods_for_Nuclear_Export_Sequence_Identification.pdf | Public | Download |
Permanent link to this page: https://digital.wpi.edu/show/db78tf48x