- ProteinShake
ProteinShake provides one-liner imports of large scale, preprocessed protein structure tasks and datasets for various model types and frameworks.
- SAT
SAT provides a class of simple and flexible graph transformers built upon a new self-attention mechanism, which incorporates structural information into the original self-attention by extracting a subgraph representation rooted at each node before computing the attention. SAT can leverage any existing GNN to extract the subgraph representation and systematically improve the peroformance relative to the base GNN.
- GraphiT
GraphiT is an instance of transformers designed for graph-structured data. It takes as input a graph seen as a set of its node features, and incorporate the graph structure via i) relative positional encoding using kernels on graphs and ii) encoding local substructures around each node, such as short paths, before adding it to the node features.
- OTK
Optimal transport kernel (OTK) is a kernel for feature aggregation. It allows to perform adaptive pooling (attention + pooling). Principally, it can be useful to model any data represented as sets of features (sequences, images, graphs etc.). In this implementation, it can be used as a module in neural networks, or alone as a kernel method.
- GCKN
Graph convolutional kernel networks (GCKN) is a software package to model graph-structured data.
- RKN
Recurrent kernel networks (RKN) is a software package to model biological sequences (DNA, protein etc.) with potentially gapped motifs.
- CKN-Pytorch-image
CKN-Pytorch-image is a software package to perform image classification with convolutional kernel networks.
- CKN-seq
CKN-seq is a software package to model biological sequences (DNA, protein etc.) with convolutional kernel networks.