Carlo Melendez

MIMIR is a deep learning framework that imputes missing modalities and features in multi-omic data using shared representation learning, outperforming existing methods on pan-cancer data.

The paper modifies the Casanovo model to account for digestion enzyme bias but finds no significant improvement in sequencing accuracy.

Casanovo translates tandem mass spectra into peptide sequences using a transformer model, outperforming existing methods and improving analysis in immunopeptidomics and metaproteomics.