Login
Differences
This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision | |||
|
teaching:ss2024:dl4science:summaries [2024/05/08 17:26] Karnik Ram |
teaching:ss2024:dl4science:summaries [2024/06/14 17:15] (current) Karnik Ram |
||
|---|---|---|---|
| Line 11: | Line 11: | ||
| Alternate title: Message Passing GNNs are all you need for solving PDEs | Alternate title: Message Passing GNNs are all you need for solving PDEs | ||
| + | |||
| + | |||
| + | ----- | ||
| + | |||
| + | __Summary on GraphCast: Learning skillful medium-range global weather forecasting **by Nils**__ | ||
| + | |||
| + | GraphCast rivals more traditional numerical medium range weather forecasting methods using a GNN approach. It uses the worlds weather data as 0.25 degree grid points across earth as its input. It then autoregressively learns to predict the weather in 6h hour time intervals via a message passing GNN architecture. The most unique part of this architecture is that the grid points are mapped to a so called multi-mesh via the encoder and mapped back from the multi-mesh for the actual predictions. The multi-mesh can be thought of as an isocahedron for which each side is continually split into more triangles (in this case repeated 6 times) resulting in a graph with over 40.000 nodes. However, this graph maintains all the edges from the intermediate splits, i.e. all the edges of the original isocahedron, | ||
| + | |||
| + | ----- | ||
| + | |||
| + | __Summary on Improved protein structure prediction using potentials from deep learning **by Ferdinand**__ | ||
| + | |||
| + | A novel method to predict protein structures is presented, which, contrary to most previous approaches, never relies on templates of homologous proteins, making it more useful for predicting unknown proteins. The prediction of a specific protein structure is performed by gradient descent on pairwise distances of protein residues, whilst a CNN is trained to predict these distances in the form of a histogram of pairwise distances. The training data was based on the Protein Data Bank, but used HHblits to construct/ | ||
| + | |||
| + | ----- | ||
| + | |||
| + | __Summary on Highly accurate protein structure prediction with AlphaFold **by Sarthak**__ | ||
| + | |||
| + | Let's start the journey. We assume modelling long-range dependencies over MSAs with vanilla transformers would suffice, since that would be the correct inductive bias for reasoning over a protein sequence, it doesn' | ||
| + | |||
| + | Since they need to represent edges, they add a bias term to the row attention for the MSA, which encodes the pairwise relationships (edges), they search structure data for this. We may think doing so once suffices, it doesn' | ||
| + | |||
| + | Now we may think that picking the top row and the 2D pairwise would suffice for building the 3D representation, | ||
| + | |||
| + | This suffices for the backbone, but what about the side chains? | ||
| + | |||
| + | They take the first row of the MSA to predict torsion angles with it, which combined with the backbone through local transformations gets them the side chains. We now have a final representation and a ground truth with a loss function. We think a simple loss function would of course suffice, not true, they include FAPE Loss (chirality aware loss), auxiliary loss, distribution loss, MSA loss and confidence loss. We assume doing so once would suffice, it doesn' | ||
| + | |||
| + | Alternate title: Unfolding AlphaFold2 | ||
| + | |||
