igraph

Use when working with GraphSAGE — Graph Sample and Aggregate — an inductive graph neural network framework for learning node embeddings on large graphs. Applies to biological network analysis: protein

3 shared topics • 1 shared operation

Use when working with DGL (Deep Graph Library) — the Python framework for building and training graph neural networks (GNNs). Apply GCN, GAT, GraphSAGE, GIN, and other GNN architectures to biological

2 shared topics • 2 shared operations

NCBI/NIH tools — search, retrieve, and process biological data from NCBI databases (PubMed, GenBank, Gene, SRA, RefSeq, ClinVar, dbSNP, Taxonomy) using the NCBI E-utilities REST API and NCBI Datasets

2 shared topics • 2 shared operations

Use when working with PyG (PyTorch Geometric) — the PyTorch-based library for deep learning on graphs and irregular data structures. Supports graph neural networks (GNNs) including GCN, GAT, GraphSAGE

2 shared topics • 2 shared operations

Wm82-tools covers bioinformatics workflows for the Glycine max (soybean) Williams 82 (Wm82) reference genome — the primary reference for soybean genomics. Use cases: downloading and indexing Wm82 geno

2 shared topics • 2 shared operations