🏆 Our Awards
Due to some mistake during the user sign up, our undergraduate team was wrongly classified into the Overgrad category.
However, even as undergraduates, we still won the Silver Medal, and 2 amoung 3 of the Gold Medal requirements; Nomination for special prize 🔗Best New Basic Part and village award 🔗Best Diagnostics Project in the Overgrad category in the Overgrad category!
We are proud to announce that we have won the following awards!
🥈 Silver Medal
We won the Silver Medal for our outstanding performance!
What's more, for the Gold Medal, we had archieved 2 amoung 3, including Excellence in: 🔗New Basic Part and 🔗Software Tool!
Eligible 😀
Overgrad Silver Medal
Eligible 😀
Ineligible 😫
citation: the table above is taken from igem official website
🏅 Special Prize: Best New Basic Part, Overgrad Nomination
We were nominated for the 🔗Best New Basic Part in the Overgrad category!
🏅 Village Award: Best Diagnostics Project, Overgrad Nomination
We were nominated for the 🔗Best Diagnostics Project in the Overgrad category!
🎥 Checkout our Village Video!
Below is the village video for the 2023 jamboree! Checkout the awesomeness of the village!
🎤 Judgers' voice
This DNA-based neural network seems like an important Foundational Advance that can be used by other teams for similar quantification-based decision trees. I hope the team continues this work and also widely shares and publishes this system for others to build on.
great job overall integrating both the wet lab and dry lab portions. The multiplexing added novelty to the strand displacement reactions. The in-silico work was outstanding.
You have done a great job with your machine learning tool, effectively demonstrating its functionality. Furthermore, your integration of human practice into your work and collaboration with a wide range of stakeholders to share information on synthetic biology is praiseworthy. The "What, How, and Will" research model you employed has made it clear how you communicated with various stakeholders and how it influenced your project. Best of luck with your future research endeavors!
Congratulations of a very interesting approach to a general diagnostics problem. You made an impressive use of a neural network to this diagnostics problem, with great potential for a variety of uses, including patient recovery monitoring.
Creating an in-silico diagnostic neural network and converting that to a biological system is extremely innovative. Looking for human biomarkers instead of viral/bacterial pathogens is a creative way to get around what would be a biosafety concern. I love taking a complicated paper (Lopez 2018), simplifying the implementation, and improving on it with negative weights. Your human practices is incredibly thorough and went above and beyond what was necessary. You probably could have gotten excellence in education and model on top of your stellar work elsewhere.