Comments (5)
Hi,
Thanks for your interest. You can find positive training and negative training through the links below. https://drive.google.com/file/d/1_pf6xIK2dRql_zZ5A1BzoGvWIYVvEaBp/view?usp=sharing https://drive.google.com/file/d/1KLlH-CBS4ep6UAEeh4Zv9Eghk1hZUWj7/view?usp=sharing
Thanks!
from pmtnet.
Hi @tianshilu
I have a similar request. I'd like to make some comparisons on the method you proposed. Could you provide the testing data used in pMTnet with both pos/neg labels? Thank you!
from pmtnet.
Hi @Miles-DDD,
Please find the testing data with labels through the links below:
https://drive.google.com/file/d/1iddT16YEbEh5LYULokEMoey53RPiVsXt/view?usp=sharing
https://github.com/tianshilu/pMTnet/blob/master/test/input/test_input.csv
Thanks!
from pmtnet.
Hi @tianshilu
Thank you for providing all the information!
I am curious about how the negative sets are generated (like any script?), as I have found 1912 entries are overlapping in the positive and negative training sets as the following command. Not sure if I have made any mistakes here.
comm -12 <(sort -u neg_training.csv ) <(sort -u pos_training.csv ) | wc -l
Also, I would like to know how these labeled training/ test data contribute to the training_data.csv and testing_data.csv under the pMTNet/data repository.
from pmtnet.
Hi @madnessfish,
Thanks for your interest in our study! For each pair of TCR-pMHC, 10 negative pairs are generated by sampling 10 TCRs from the other TCRs randomly. So, there is a very small proportion overlapping between positive and negative by chance. We didn't remove the overlapped pairs from the negative dataset because they help reduce overfitting.
Negative datasets are generated from the training_data.csv and testing_data.csv as I described above. Hope this helps!
Tianshi
from pmtnet.
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
- Strange characters in the testing_data.csv and training_data.csv HOT 1
- HLA Pseudosequence generation HOT 3
- 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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