This project focuses on exploring the relationship between biological activities and temperature, aiming to study this connection from both micro and macro perspectives to contribute to the advancement of synthetic biology. From micro level, we used the Hyena framework to build our own high-precision model, HEATMAP-AI, to predict the optimal temperature of enzymes. From macro level, we constructed an etcGEM to simualte the growth situation of organisms in different temperature.We hope that users from various backgrounds will benefit from our work.
Click the users below to find more information.
To make the deep learning model easier to use, we have designed a dedicated webpage where users can conveniently predict the optimal temperature of any enzyme.
To use the deep learning model, please visit our Website.
The tutorials are specifically shown in the Software page, please visit the AI section.
We have uploaded our software to the GitLab repository, which can be downloaded by users looking forward to predict locally. Extended usage information is available in our README documentation.
We have uploaded our ecGEM to the GitLab repository, which can be downloaded by users looking forward to predict locally. Extended usage information is available in our README documentation.
We have uploaded our etcGEM to the GitLab repository, which can be downloaded by users looking forward to predict locally. Extended usage information is available in our README documentation.
Additionally, to make our dataset more accessible, we have created a platform where users can easily search for and download enzyme data.
To access the database, please visit our Website.
The tutorials are specifically shown in the Software page, please visit the AI section.