Xiaowen (Kevin) Liu, Department of Medicine
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein functions in biological tissues. Many deep learning methods have been developed for de novo peptide sequencing, i.e., predicting the peptide sequence for a mass spectrum. However, the prediction accuracy is still not high. Here we propose to develop new deep learning models and algorithms to increase the accuracy in de novo peptide sequencing.
Build a deep learning model for accurate peptide de novo peptide sequencing using bottom-up mass spectrometry data.
Time, eligibility, and other details
| Expected workload | 10 hours per week; deep learning model development and training, paper writing |
| Skills required | Python programming, machine learning, algorithms |
| Who is eligible | Computer Science |
| Core partners | |
| Sponsoring party | This is a faculty project. |
| Volunteer, Paid, or Credit-eligible? | Volunteer |
| Forms Required | CV |
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