Difference between revisions of "Team:SKLMT-China/SoftWare Overview"
| (5 intermediate revisions by the same user not shown) | |||
| Line 47: | Line 47: | ||
<h2 class="title" id="s1">Open Source Software</h2> | <h2 class="title" id="s1">Open Source Software</h2> | ||
| − | <p>We have hosted our DePro Web on Aliyun Server, and put the back end Python script on | + | <p>We have hosted our DePro Web on Aliyun Server, and put the back end Python script on GitHub with a <code>MIT License</code>, which means everyone could use and modify our software to make it more helpful and efficient. And we would be gald to see you use and imporve our DePro software, and links below are our project on git and you could view our source or documents here:</p> |
| − | + | <div style="text-align:center"> | |
| − | <a href="https://github.com/SKLMT-China/Depro"><i class="fa fa-github fa-4x"></i></a> | + | <a href="https://github.com/SKLMT-China/Depro"><i class="fa fa-github fa-4x"></i>DePro on GitHub</a> |
| − | <a href="https://github.com/SKLMT-China/Depro/blob/add-license-1/LICENSE">MIT License File</a> | + | <p>Read our <a href="https://github.com/SKLMT-China/Depro/blob/add-license-1/LICENSE" style="color: #00bfff">MIT License File</a>.</p> |
| + | </div> | ||
</div> | </div> | ||
Latest revision as of 02:53, 18 October 2018
introduction
This year, the team SKLMT-China established a useful software tool based on our own wet lab results to help people easily search and predict a proper promoter for fine-tuning gene expression in the synthetic study. To make the
Our Depro system is a platform for promoter researchers to exchange data and share results. Everyone can benefit from the test results of previous researchers or enrich our software with new test data to improve the accuracy of the fit. Through our software, you can:
(1) Predict a new promoter’s strength. Our program is based on all known data for the deep learning of supervised training. you are merely asked to input the core sequence of the promoter, we will immediately calculate the strength level of the promoter
(2) Use a new set of data to enrich the program's deep learning process. Your data will be imported into our database and will accompany our program to complete every deep learning in the future
(3) Freely set the number of times to learn. You are free to decide how quickly and precisely your program will run to meet your needs
Open Source Software
We have hosted our DePro Web on Aliyun Server, and put the back end Python script on GitHub with a MIT License, which means everyone could use and modify our software to make it more helpful and efficient. And we would be gald to see you use and imporve our DePro software, and links below are our project on git and you could view our source or documents here:
Read our MIT License File.