Comments (3)
Hi @Hugh-OBrien ,
Thank you for carefully looking into the code and pointing out the problem! After reviewing the pseudo-seq method in both netMHCpan and MHCflurry, I think you are right that we need to do alignments for HLA sequences before taking pseudo sequences. Even though we had the pseudo-sequences not as same as netMHCpans, the performance is still good compared to netMHCpan. I will comment on this caveat in the code. We are also working on pMTnet version 2.0. We will take this caveat into consideration. Thank you again for looking into this and letting us know!
Tianshi
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Thank you for the reply. Yes on my quick retraining test I found there was a modest performance hit on the MHC-peptide binding affinity; however, as this is only the encoder branch it may not end up affecting the overall performance of the overall model in a significant way. Would be good to add in a caveat in a comment as it may increase the performance of future work
from pmtnet.
I totally agree with you. Thanks again for your input!
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Related Issues (20)
- TCR_encoder_30.h5 and other trained models in the repo HOT 1
- tensorflow.python.framework.errors_impl.InvalidArgumentError: input and filter must have the same depth: 76 vs 30 HOT 4
- A bug may be in pMTnet HOT 2
- Labelled training data used in pMTnet HOT 5
- Strange characters in the testing_data.csv and training_data.csv HOT 1
- Full TCR sequences of validation data HOT 2
- Prediction with unknown antigen HOT 1
- Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. HOT 4
- Prediction with unknown HLA HOT 2
- Retrain Model pMTnet HOT 3
- How to calculate the AUC?
- How to filter the result by Rank HOT 1
- What does the negative rank mean HOT 1
- about the testing data
- Request for the support of MHC II molecule like "DRA1*02:01" HOT 2
- Understanding the model predictions- not CDR3 rank not influenced by HLA or peptide HOT 1
- Build my own background TCR HOT 2
- About Training TCR_encoder
- Error Predict Score on Neg_10k Data
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