Author

Emily Franklin

University of Toronto

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Hannes Luc RöstiD· 1 shared

Activity by Year

2022-2022 · peak 1/yr

1 paper

Recent·Most cited

Comparing Machine Learning Architectures for the Prediction of Peptide Collisional Cross Section

Emily Franklin, Hannes Luc Röst

The paper compares LSTM, CNN, and transformer models for predicting retention time and collisional cross section in mass spectrometry data, finding LSTM and CNN perform similarly while the transformer underperforms.

bioRxiv · Mar 1, 2022 · PreprintBioinformatics