Open bertranMiquel opened 4 months ago
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Hello, @bertranMiquel ! Thank you for your submission. As we near the end of the challenge, I am collecting participant info for the purpose of selecting and announcing winners. Please email me (or have one member of your team email me) at guillermo_bernardez@ucsb.edu so I can share access to the voting form. In your email, please include:
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Hi Guillermo,
Answering to fulfill the submission requirements:
Best, Bertran
Missatge de levtelyatnikov @.***> del dia dl., 8 de jul. 2024 a les 18:47:
Hello, @bertranMiquel https://github.com/bertranMiquel ! Thank you for your submission. As we near the end of the challenge, I am collecting participant info for the purpose of selecting and announcing winners. Please email me (or have one member of your team email me) at @.*** so I can share access to the voting form. In your email, please include: • your first and last name (as well as any other team members) • the title of the method you implemented • the input domain of the method you implemented • the output domain of the method you implemented • your pull request number (#37 https://github.com/pyt-team/challenge-icml-2024/pull/37 ) Before July 12, make sure that your submission respects all Submission Requirements laid out on the challenge page. Any submission that fails to meet this criteria will be automatically disqualified.
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The UniProt dataset is a custom dataset that is created by fetching data from the UniProt API. The dataset is created by fetching a list of proteins based on a query and then fetching the structure of each protein using the AlphaFold API. The dataset is then created by creating a graph for each protein where the nodes are the residues and edges are the connections between residues. These connections are usually done by the closeness of the residues. In this example, we connect the residues in two ways, representing the data into a graph:
The target variable is the mass of the protein.
This representation can be improved by lifting it to an hypergraph. As done in Jiang et al. (2021), we will create an hypergraph by grouping the connected residues that are close to each other (less than a parameter).
This pull request is done under the team formed by: Bertran Miquel Oliver, Manel Gil Sorribes, Alexis Molina