PUMA: Promoter Unraveling through Machine-learning Algorithms

Team Members

• Carolus Vitalis
• Juan Hanel
• Tyler Amos

Faculty Advisors

• Daniel Acuña – Science of Science and Computational Discovery Laboratory
• Chris Myers – Genetic Logic Lab

Project Overview

PUMA aims to develop an AI model that can identify promoter sequences in existing databases. This project is designed to overcome the challenges of misclassification of genetic parts in repositories like the iGEM Part Repository or SynBioHub, ensuring the accuracy and reliability of the genetic parts.

Contributing

Any contributions you make are greatly appreciated.

1. Fork the project.
2. Create your Feature Branch git checkout -b newFeature
3. Commit your Changes git commit -m 'Add some newFeature'
4. Push to the Branch git push origin newFeature
5. Open a Pull Request

Contact

Carolus Vitalis – [email protected]
University of Colorado Boulder