Difference between revisions of "Team:SKLMT-China/Supervised NN"
Zhaowenxue (Talk | contribs) (Created page with "<html> <div class="container" id="section-2"> <div class="paragraph shadow"> <h2 class="title">Supervised NN</h2> <p>test</p>...") |
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| − | <h2 class="title"> | + | <h2 class="title">principle</h2> |
| − | <p> | + | <p>First, we processed the update module of the data. We use python language to process the input of DNA sequences, and use the coding format of the triple base to encode the core sequence. Subsequently, 65 data of the occurrence frequency and promoter strength of the AAA~GGG sequence are input into the database through library “<i>pymysql</i>”.</p> |
| + | <p>Next, we dealt with the part of deep learning. After users set learning generation on the Internet, our program will automatically retrieve all the data stored in the database, calculate all parameters according to the model of supervised training, and finally export the calculation results to the database for future use.</p> | ||
| + | <p>Finally, we provide an interface for predicting promoter strength. The user only needs to input the core sequence, and our program will read all the parameters obtained after deep learning from the database and calculate the strength of the promoter through the neural network structure we present. If the user has added new data before, re-complete the deep learning before the prediction session to feed the added data back to the results. | ||
| + | </p> | ||
Latest revision as of 00:08, 17 October 2018
principle
First, we processed the update module of the data. We use python language to process the input of DNA sequences, and use the coding format of the triple base to encode the core sequence. Subsequently, 65 data of the occurrence frequency and promoter strength of the AAA~GGG sequence are input into the database through library “pymysql”.
Next, we dealt with the part of deep learning. After users set learning generation on the Internet, our program will automatically retrieve all the data stored in the database, calculate all parameters according to the model of supervised training, and finally export the calculation results to the database for future use.
Finally, we provide an interface for predicting promoter strength. The user only needs to input the core sequence, and our program will read all the parameters obtained after deep learning from the database and calculate the strength of the promoter through the neural network structure we present. If the user has added new data before, re-complete the deep learning before the prediction session to feed the added data back to the results.