Dear Mohammad Al-baruni Chowdhury,

Share the problems, so that we can find suitable solutions to resolve them

Mohammad Al-baruni Chowdhury Hamed Zahraee

protein-protein interactions help:

1. **Adjust the data inputs**: The `roc()` function in the `pROC` library expects a binary classification outcome, such as "binder" vs. "non-binder". In the case of protein-protein interactions, you may need to preprocess your data to create a suitable binary classification problem.

2. **Consider alternative metrics**: While the AUC (Area Under the Curve) is a popular metric for evaluating the performance of a binary classifier, it may not be the most appropriate metric for protein-protein interaction studies. You may want to explore other metrics, such as:

- Precision-Recall (PR) curves and the associated AUC (PR-AUC)

- F1-score

- Matthews Correlation Coefficient (MCC)

These metrics may provide more meaningful insights for your protein-protein interaction analysis.

3. **Explore specialized libraries**: There are some specialized libraries and tools designed for protein-protein interaction analysis, which may provide better support and functionality compared to the general-purpose `pROC` library. Some examples include:

- `BioNetStat`: A R package for the analysis of biological networks, including protein-protein interactions.

- `PPISURV`: A R package for protein-protein interaction network-based survival analysis.

- `NetworkAnalyst`: A comprehensive web-based platform for network-based analysis, including protein-protein interaction studies.

4. **Seek expert guidance**: If you're still facing difficulties, it may be helpful to consult with researchers or experts who have experience in the field of protein-protein interaction analysis. They may be able to provide more specific guidance and recommendations based on your particular use case.

Hope it helps; partial credit AI