ogbn_proteins — Protein associations of 8 species (ogbn-proteins, 2020)

Description

An undirected network of functional associations between the proteins of eight species, from the ogbn-proteins dataset of the Open Graph Benchmark (OGB), a benchmark for node classification (version 1, of May 2020). The proteins and associations come from the STRING database (version 11): an edge means that two proteins are functionally associated, and has as features the scores of eight kinds of evidence for the association. The species are Escherichia coli (taxonomy identifier 511145), Arabidopsis thaliana (3702), Saccharomyces cerevisiae (4932), Caenorhabditis elegans (6239), Drosophila melanogaster (7227), Danio rerio (7955), Homo sapiens (9606) and Mus musculus (10090); the networks of all the species of STRING are in the entry string_all.

The task of the benchmark is to predict the presence of 112 protein functions (terms of the Gene Ontology), given as binary labels of the nodes. The nodes are split by species: the proteins of one species are used for validation, those of another for testing, and the rest for training.

The network has the following properties:

protein_id: the identifier of the protein in STRING, e.g. 9606.ENSP00000000233 (the NCBI taxonomy identifier of the species, followed by the identifier of the protein).
species: the NCBI taxonomy identifier of the species of the protein.
labels: the 112 binary labels of the protein (1 if the protein has the function, 0 otherwise), in the order of the graph property label_go_terms.
split: the part of the benchmark the protein belongs to: train, valid or test.
Edge homology, neighborhood, fusion, cooccurence, coexpression, experimental, database, textmining: the scores of the eight kinds of evidence for the association (between 0 and 1; 0 if there is no evidence of that kind), as in STRING: homology, conserved genomic neighborhood, gene fusions, phylogenetic co-occurrence, co-expression, experiments, curated databases and text mining (as named by STRING, with its spelling of cooccurence).
label_go_terms (graph): the Gene Ontology term of each label, e.g. GO:0003674.

Tags

Biological Protein interactions Weighted Metadata

Citation

W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, J. Leskovec, "Open Graph Benchmark: Datasets for Machine Learning on Graphs." Advances in Neural Information Processing Systems 33, 22118-22133 (2020) 10.48550/arXiv.2005.00687 Sci-Hub
D. Szklarczyk et al., "STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets." Nucleic Acids Research 47, D607-D613 (2019) 10.1093/nar/gky1131 Sci-Hub

Upstream URL OK

https://ogb.stanford.edu/docs/nodeprop/#ogbn-proteins
https://snap.stanford.edu/ogb/data/

Upstream license

CC0 1.0, https://creativecommons.org/publicdomain/zero/1.0/

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
ogbn_proteins	132,534	39,561,252	597.00	621.48	2291.21	28.57	0.36	0.28	10	1.00	Undirected	Unipartite	protein_id species labels split	homology neighborhood fusion cooccurence coexpression experimental database textmining	128.6 MiB	347.4 MiB	309.2 MiB	264.1 MiB