Comments (4)
Hi @hanulpark98, no worries :)
Those lines you refer to are, you can think of as placeholder variables for selected function, meaning
DeepDTA/source/run_experiments.py
Line 530 in 2c9cbaf
perfmeasure
variable can take any function as long as these functions have the same inputs which is Y
and P
. If you recursively follow the functions where perfmeasure
is sent as argument you will reach to below line.
DeepDTA/source/run_experiments.py
Line 369 in 2c9cbaf
The code is a little bit old now but idea is to give it a little more flexibility whether you choose get_cindex
or get_aupr
functions from emetrics.py
. Hope this makes sense.
You can refer to here for pytorch implementation.
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oh that makes sense
big thanks for the comment
from deepdta.
Hi @hanulpark98, no worries :)
Those lines you refer to are, you can think of as placeholder variables for selected function, meaning
DeepDTA/source/run_experiments.py
Line 530 in 2c9cbaf
perfmeasure
variable can take any function as long as these functions have the same inputs which isY
andP
. If you recursively follow the functions whereperfmeasure
is sent as argument you will reach to below line.DeepDTA/source/run_experiments.py
Line 369 in 2c9cbaf
The code is a little bit old now but idea is to give it a little more flexibility whether you choose
get_cindex
orget_aupr
functions fromemetrics.py
. Hope this makes sense.You can refer to here for pytorch implementation.
hi i've been studying your code since and found out that 11902 proteins * 1353 ligands binding affinity data of your dataset Y, there were only 50181 (which is 0.3 percent of the data) that were not NAN. can you explain how it is possible to train the data with so much missing data?
from deepdta.
Hi @hanulpark98, it is true that actual experimental data is quite limited and this eventually is an imbalanced dataset. Therefore, one can pay attention to metrics such as AUPR to interpret the results more carefully. Hope this answers your question. I'm closing the issue but feel free to comment/reopen.
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Related Issues (20)
- Prediction of binding affinity using DeepDTA interpretation HOT 3
- Complete Code HOT 1
- Running "S-W and Pubchem-Sim" case from DeepDTA HOT 3
- the question about the two datasets. HOT 2
- Question about KIBA score HOT 4
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- some questions HOT 1
- Could the pre-trained model be made available? HOT 2
- Help with retraining the combined model with a different dataset HOT 2
- new datas HOT 9
- Reimplementation of DeepDTA with Pytorch HOT 2
- I have some questions, can you help me!!! HOT 1
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- During implementation on my own dataset, I got weird results HOT 4
- Regarding data leakage caused by amino acid sequences in the data set HOT 1
- A question about Davis dataset HOT 1
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