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 | Jul 1, 2023 |
No matching items
Fragmentation
| Title | Author | Date |
|---|---|---|
| NIST (part 1): Preparing a spectral library for ML | Jul 1, 2023 | |
| NIST (part 2): Traditional ML: Gradient boosting | Jul 1, 2023 | |
| NIST (part 3): Deep learning: BiLSTM | Jul 1, 2023 | |
| Prosit-style GRU with pre-annotated ProteomeTools data | Jul 1, 2023 | |
| Raw file processing with PROSIT style annotation | Jul 1, 2023 |
No matching items
Ion mobility
| Title | Author | Date |
|---|---|---|
| Predicting CCS values for TIMS data | Jul 1, 2023 |
No matching items
Retention time
| Title | Author | Date |
|---|---|---|
| DLOmix embedding of Prosit model on ProteomeTools data | Jul 1, 2023 | |
| Manual embedding of Bi-LSTM model on ProteomeTools data | Jul 1, 2023 | |
| Preparing a retention time data set for machine learning | Jul 1, 2023 | |
| Transfer learning with DeepLC | Jul 1, 2023 |
No matching items