Open wq-ls opened 5 months ago
Hi @wq-ls sorry for the later reply.
I have to say that I don't have enough expertise in this domain to answer your question here. This repo is my best effort to replicate the paper result.
I will recommend
I strongly agree with your suggestion, because I did encounter a lot of problems when I continued to conduct subsequent analysis after predicting the results according to my method.
I should reprocess my dataset to make sure it's accurate.
Finally, thank you very much for your reply.
Hi, @egochao
It's very exciting that you provide a simple method to train and predict promoter sequences. This is friendly to us newbies in machine learning. I encountered some problems during use and would like to ask you for some advice.
For example, I combined promoter sequences identified in more than 60 species of fish and trained a model. I want to use this trained model to predict promoter regions of other fish species, is this possible?
The final prediction result log: for fish-TATA, 46800 sequences, Test precision: 0.9281830539193475;Test recall: 0.8752403332621235;Test MCC :0.8088365907172335 for fish-nonTATA, 77245 sequences, Test precision: 0.934273019764057;Test recall: 0.7893851132686084;Test MCC :0.7428381575465615
This is only the result of one training session. Compared with the training results of mouse and human models, it seems not very good. I am also trying to increase the number of trainings.
Can you give me some suggestions on other parameters that need to be adjusted? For example, the “I used the final set of parameter from the paper. Kernel size = [27, 14, 7], and maxpooling with kernel = 6” you use. I'm a little confused whether or not I should adjust it for my data, and how?
But this also raised another issue: There may be inaccurate promoter sequences in the data set I constructed myself. For example, it is not a real TATA-box sequence. I put this into the data set for training.
After all, the data is not as reliable as coming from the EPDnew database. But I can estimate that about 90% of the sequences are relatively accurate.
Will this have a big impact on the final model training? How do I do the assessment? Considering that there are errors in the data set itself, what should the final precision, recall, and mcc reach to be considered a suitable model?
Best wishes,
Shuo Li