Summary
Nuclear magnetic resonance (NMR) and Mass spectrometry (MS) are two mainly used techniques in metabolomics that can measure abundance of hundreds of metabolites. The data generated by either technique have already allowed a huge progress in various research fields from personalized medicine to pharmaceutical sciences. Combining measurements from both platforms through a simple concatenation also led valuable discoveries. In this work, however, we propose an alternative data fusion approach based on deep learning that aims to make use of each type of measurement and combine the derived information in a different layer for disease prediction.
Responsibilities*
- Work with metabolomics data and assist in identifying factors to certain diseases such as ALzheimer's or Parkinsons
- Preprocess data regardless of its modality or nature and build prediction models
- Maintain and fine tune the models built
- Attend weekly meetings to report progress
Required Qualifications*
- Currently enrolled undergraduate student in good academic standing or recently graduated from an undergraduate program and not currently enrolled in a graduate program
- Machine learning and/or data mining skills
- Strong Python programming skills
- Some experience with PyTorch or Tensorflow frameworks
Desired Qualifications*
Experience with deep leaning is preferred.
Work Schedule
Work on this project will take place during the Summer 24 and Fall 24 semesters.
Additional Information
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