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Hi,
Thank you for the amazing work!
I am curious about the results in table 8. Why most models (other than GNN) perform dramatically worse in the 60% identity split than in the 30% identity split? Intuitively, the task with 60% split should be easier and achieve better performance as there is more similarity between protein sequences.
The text was updated successfully, but these errors were encountered:
Gloria-LIU
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Why models perform worse in the 60% identity split than in the 30% identity split on LBA tasks?
Why models perform worse on the 60% identity split LBA dataset than on the 30% split?
Jul 29, 2022
Hi, Thank you for the amazing work! I am curious about the results in table 8. Why most models (other than GNN) perform dramatically worse in the 60% identity split than in the 30% identity split? Intuitively, the task with 60% split should be easier and achieve better performance as there is more similarity between protein sequences.
I agree with your theoretical guess. However, not only atom3d but also some other following studies show the similiar phenomenon. For instance, the table below is copied from Multi-Scale Representation Learning on Proteins
Hi,
Thank you for the amazing work!
I am curious about the results in table 8. Why most models (other than GNN) perform dramatically worse in the 60% identity split than in the 30% identity split? Intuitively, the task with 60% split should be easier and achieve better performance as there is more similarity between protein sequences.
The text was updated successfully, but these errors were encountered: