This final competition focuses on multi-label prediction of cysteine-related post-translational modifications (PTMs) from short protein sequence fragments using machine learning models.
Given a fixed-length protein fragment, participants are asked to determine whether specific cysteine modifications occur within the sequence.
Multi-label classification where each sequence may have zero, one, or multiple PTMs.
Cysteine-related PTMs involved in redox regulation and membrane association.
Learn sequence-level patterns to accurately predict PTM occurrence.
The final test dataset contains only input sequences. Participants must submit prediction results in a CSV file following the required schema for automatic evaluation.
This file contains only sequence information. No label columns are provided.
ID |
Sequence |
|---|---|
| Q0GA42 | AAAAAAAALGVRLRDCCSRGAVLLLFFSLSP |
| Q9NV92 | AAAAAETSQRIQEEECPPRDDFSDADQLRVG |
| A2AMH3 | AAARLVSGYDSYGNICGQRNAKLEAIPNSGL |
Prediction columns must contain binary values (0 or 1).
ID |
Sequence |
S-glutathionylation |
S-nitrosylation |
S-palmitoylation |
|---|---|---|---|---|
| Q0GA42 | AAAAAAAALGVRLRDCCSRGAVLLLFFSLSP | 0 | 0 | 0 |
| Q9NV92 | AAAAAETSQRIQEEECPPRDDFSDADQLRVG | 0 | 1 | 0 |
| A2AMH3 | AAARLVSGYDSYGNICGQRNAKLEAIPNSGL | 1 | 1 | 1 |