Tutorials
On ProteomicsML you will find detailed tutorials outlining how to work the latest state-of-the-art machine learning models, and even how to turn your own raw data into a suitable format. Explore all tutorials on ProteomicsML and click the “Open in Colab” badge to interact with the notebooks in a userfriendly coding environment.
Detectability
| Title | Author | Date |
|---|---|---|
| Modelling protein detectability with an MLP | Eric Deutsch | Feb 16, 2023 |
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Fragmentation
| Title | Author | Date |
|---|---|---|
| NIST (part 1): Preparing a spectral library for ML | Ralf Gabriels | Feb 16, 2023 |
| NIST (part 2): Traditional ML: Gradient boosting | Ralf Gabriels | Feb 16, 2023 |
| NIST (part 3): Deep learning: BiLSTM | Ralf Gabriels | Feb 16, 2023 |
| Prosit-style GRU with pre-annotated ProteomeTools data | Siegfried Gessulat | Feb 16, 2023 |
| Raw file processing with PROSIT style annotation | Tobias Greisager Rehfeldt | Feb 16, 2023 |
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Ion mobility
| Title | Author | Date |
|---|---|---|
| Predicting CCS values for TIMS data | Robbin Bouwmeester | Feb 16, 2023 |
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Retention time
| Title | Author | Date |
|---|---|---|
| DLOmix embedding of Prosit model on ProteomeTools data | Tobias Greisager Rehfeldt | Feb 16, 2023 |
| Manual embedding of Bi-LSTM model on ProteomeTools data | Tobias Greisager Rehfeldt | Feb 16, 2023 |
| Preparing a retention time data set for machine learning | Robbin Bouwmeester | Feb 16, 2023 |
| Transfer learning with DeepLC | Robbin Bouwmeester | Feb 16, 2023 |
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