Thsi may already have been implemented.
In accordance with
https://getsatisfaction.com/eternagam…
- To achieve high binding energies, we need:
a) 10-15% C
b) 35-60 % A
c) 10-16 % U
d) 35 % G for Ex3, and 12-16 % G for Ex4 and SS1
and for the second data set
- To achieve high Eternascores, we need:
a) 10-24 % C
b) 25-58 % G
c) 8-25 % U
d) 18-40 % G
!
These spans are, unfortunately, so wide that they likely encompass 3/4 of all the molecules in this (or any other) data set.
Of particular interest are those runaway data lines (e.g. at 38 % A and corresponding 14 % U for Ex3), where a whole set of very high binding energies can be achieved with essenstially the same composition, and just one minute change in the molecule makes a large difference, whereas any such change in molecules outside this very defined parameter set changes the binding energy barely at all. Similar peaks exist for other experiments, too, but I wanted to point this one out as it’s very prevalent across the first four graphs.
1 Like
And, in accordance with
https://getsatisfaction.com/eternagam…
C% must be < 20% and should be 10-14%
G% should be < 30% and, even better, should be 15-25%, and must be < 25%, and should be 8-25%, and for a better score, should be <20%
A% should be 18-60%, and for a better score should be 18-40%, and for even better scores should be around 40%
There’;s a typo in the header:
2) To achieve high Eternascores, we need:
a) 10-24 % C
b) 25-58 % A!!!
c) 8-25 % U
d) 18-40 % G
IGNORE, delete function no longer working.
This means, combined:
https://getsatisfaction.com/eternagam…
Gaar, one typo 5 months ago and I stumble now like a little scatterbrain (well, maybe cause I am?).
Anyway, here the finalized advise, from all these ramblings:
C% must be < 24% and should be 10-14% for best scores and 10-24% for ok scores
G% must be < 40% and should be 15-25% or 18-40% (yes, I know, never good to have an “or function” in here, but these were my two outcomes - feel free to comment)
U% must be < 25%, and should be 8-25% for high scores, and 8-20% for highest scores
A% must be 25-60%, and should be 40-58% for high scores
Hi salish99,
Thank you for submitting so many strategies. I have a quick clarification question about implementing this one. Below is a summary of what I will implement in the eternabot strategy. Is there anything that I have wrong, or anything that I should add?
For example, is there a range for A% where you want to give a low award? Also, I’m confused about the G% rules - should the bot award designs with a high award or a low award? Should this award be for 15% - 40% since 15% - 25% and 18% - 40% overlap?
Penalize designs where C% is not 0% - 24%
Award designs where C% is 10% - 14%
Give low award to designs where C% is 14% - 24%
Penalize designs where G% is not 0% - 40%
Award designs where G% is 15% - 40%
Penalize designs where U% is not 0% - 25%
Award designs where U% is 8% - 20%
Give low award to designs where U% is 20% - 25%
Penalize designs where A% is not 25% - 60%
Award designs where A% is 40% - 58%
Thanks,
Vineet Kosaraju
Dear salish99,
Your strategy has been added to our implementation queue and we should have it completed within the next two to three weeks. You can check the status of the implementation here.
We will also be uploading the code used to write your strategy on this page if you are interested in reading it later.
After implementing your idea, we will post the direct link to the code here. Then, we will then optimize the parameters, and test it. At that point, we will tell you the correlation of how good your strategy was and what the new optimized parameters are.
Thanks for sharing your idea!
Vineet Kosaraju
The EteRNA Team
corrections:
Penalize designs where G% is not 0% - 40%
Award designs where G% is 18% - 25%
Give a medium award to designs where G% is 15%-18% and 25%-30%
Give a low award to designs where G% is 30%-40%
Penalize designs where A% is not 20% - 65%
Penalize a little designs where A% is not 20% - 25% or 70%-75%
Award designs where A% is 40% - 58%
C and U are good.
thanks for asking.
Dear salish99,
I am pleased to announce that a preliminary version of your strategy has been implemented with 25 lines of code. The code might slightly change in the future to remove bugs, but you can check the current version here. We will soon work on optimizing your parameters and testing the strategy.
Thank you again for sharing your switch strategy!
Vineet Kosaraju
The EteRNA Team