The overall objective of this project is to design and develop robust, reliable, and generalizable machine-learning models for peptide deduction from MS data from omics experiments. Our proposed work fills four key knowledge gaps in development of ML models, if filled, will lead to superior computational techniques capable of inferring both abundant and non-abundant peptides.
Our general strategy will involve design and development of generative models, self-learning models, biologically inspired models, and methods to infer uncertainty quantification. All this effort will fill a critical gap in our understanding and ability to deduce peptide (that are novel) and will contribute a fundamental tool for studying complex communities in proteomics, and meta-proteomics data.
At the end of this grant funding cycle, it is our expectation that we will have designed and developed highly accurate ML peptide deduction engine capable of end-to-end analysis of the MS based omics data that is robust, generalizable, and more accurate than their algorithmic counterpart.
Methods
Method development for this work is ongoing.