OpenBioLink is a resource and evaluation framework for evaluating link prediction models on heterogeneous biomedical graph data. It contains benchmark datasets as well as tools for creating custom benchmarks and evaluating models.

Documentation

Paper preprint on arXiv • Peer reviewed paper in the journal Bioinformatics (for citations) • Supplementary data

The OpenBioLink benchmark aims to meet the following criteria:

• Openly available
• Large-scale
• Wide coverage of current biomedical knowledge and entity types
• Standardized, balanced train-test split
• Open-source code for benchmark dataset generation
• Open-source code for evaluation (independent of model)
• Integrating and differentiating multiple types of biological entities and relations (i.e., formalized as a heterogeneous graph)
• Minimized information leakage between train and test sets (e.g., avoid inclusion of trivially inferable relations in the test set)
• Coverage of true negative relations, where available
• Differentiating high-quality data from noisy, low-quality data
• Differentiating benchmarks for directed and undirected graphs in order to be applicable to a wide variety of link prediction methods
• Clearly defined release cycle with versions of the benchmark and public leaderboard

The OpenBioLink2020 Dataset is a highly challenging benchmark dataset containing over 5 million positive and negative edges. The test set does not contain trivially predictable, inverse edges from the training set and does contain all different edge types, to provide a more realistic edge prediction scenario.

OpenBioLink2020: directed, high quality is the default dataset that should be used for benchmarking purposes. To allow anayzing the effect of data quality as well as the directionality of the evaluation graph, four variants of OpenBioLink2020 are provided -- in directed and undirected setting, with and without quality cutoff.

Additionally, each graph is available in RDF N3 format (without train-validation-test splits).

All datasets are hosted on zenodo.

• OpenBioLink2020: directed, high quality // RDF (default dataset for benchmarking)
• OpenBioLink2020: undirected, high quality // RDF
• OpenBioLink2020: directed, no quality cutoff // RDF
• OpenBioLink2020: undirected, no quality cutoff // RDF

Please note that the OpenBioLink benchmark files contain data derived from external ressources. Licensing terms of these external resources are detailed below.

Results are from (LinkExplorer: Predicting, explaining and exploring links in large biomedical knowledge graphs; Ott et al). Embedding approaches were trained using LibKGE. Best hyperparameters after extensive hyperparameter search can be found in the supplementary material of the before mentioned paper.

1. Install a pytorch version suitable for your system https://pytorch.org/
2. pip install openbiolink

1. clone the git repository or download the project
2. Create a new python3.7, or python3.6 virtual environment (note: under Windows, only python3.6 will work) e.g.: python3 -m venv my_venv
3. activate the virtual environment
   • windows: my_venv\Scrips\activate
     • linux/mac: source my_venv/bin/activate
4. Install a pytorch version suitable for your system https://pytorch.org/
5. Install the requirements stated in requirements.txt e.g. pip install -r requirements.txt

The OpenBioLink framework consists of three parts:

1. Graph creation
2. Dataset split
3. Evaluation

The creation of the graph and the splitting of the created graph in training, testing and an optional validation set can be performed by either via the GUI or the command line interface. The evaluation of a trained model is served as part of the openbiolink library.

By calling openbiolink from the command line a graphical user interface is started, providing an interface to create a graph and perform a dataset split. Step by step instructions on how to use the GUI can be found in the wiki.

openbiolink -p WORKING_DIR_PATH [-action] [--options] ...

To generate the default graph (with all edges of all qualifies) in the current directory, use:

openbiolink generate

For a list of arguments, use:

openbiolink generate --help

To split the default graph using the random scheme, use:

openbiolink split rand --edges graph_files/edges.csv --tn-edges graph_files/TN_edges.csv --nodes graph_files/nodes.csv

For a list of arguments, use:

openbiolink split rand --help

Splitting can also be done by time with

openbiolink split time

More documentation will be provided later.

To ensure a standardized evaluation of different methods applied to the OpenBioLink dataset, an evaluator is provided in the package openbiolink . For examples how to evaluate a model, see here.

All versions of the OpenBioLink datasets can be easily accessed within Python via the DataLoader, which downloads all required files automatically.

from openbiolink.evaluation.dataLoader import DataLoader

# Name of the Dataset, possible values HQ_DIR, HQ_UNDIR, ALL_DIR, ALL_UNDIR. Default: HQ_DIR
dl = DataLoader("HQ_DIR")

train = dl.training.mapped_triples
test = dl.testing.mapped_triples
valid = dl.validation.mapped_triples

The Biological Expression Language (BEL) is a domain specific language that enables the expression of biological relationships in a machine-readable format. It is supported by the PyBEL software ecosystem.

BEL can be exported with:

openbiolink generate --output-format BEL

Example opening BEL Script using pybel.from_bel_script():

import gzip
from pybel import from_bel_script
with gzip.open('positive.bel.gz') as file:
    graph = from_bel_script(file)

Example opening Nodelink JSON using pybel.from_nodelink_gz():

from pybel import from_nodelink_gz
graph = from_nodelink_gz('positive.bel.nodelink.json.gz')

There's an externally hosted copy of OpenBioLink here that contains the exports as BEL.

All node ID's in the graph are CURIES, meaning entities can be easily looked up online by concatenating https://identifiers.org/ with the ID, f.e.:

Detailed information of how the Identifiers are resolved can be found here https://registry.identifiers.org/

In the random split setting, first, negative sampling is performed. Afterwards, the whole dataset (containing positive and negative examples) is split randomly according to the defined ratio. Finally, post-processing steps are performed to facilitate training and to avoid information leakage.

In the time-slice split setting, for both of the provided time slices, first, negative sampling is performed. Afterwards, the first time slice (t-1 graph) is used as training sample, while the difference between the first and the second time slice serves as the test set. Finally, post-processing steps are performed to facilitate training and to avoid information leakage.

Generally, the time slice setting is trickier to implement than the random split strategy, as it requires more manual evaluation and knowledge of the data. One of the most difficult factors is the change of the source databases over time. For example, a database might change its quality score, or even its ID-format. Also, the number of relationships stored might increase sharply due to new mapping files being used. This might also result in ‘vanishing edges’, where edges that were present in the t-1 graph are no longer existent in the current graph. Although the OpenBioLink toolbox tries to assist the user with different kinds of warnings to identify such difficulties in the data, it is unfortunately not possible to automatically detect nor solve all these problems, making some manual pre- and post-processing of the data inevitable.

First, the distribution of edges of different types is calculated to know how many samples are needed from each edge type. For now, this distribution corresponds to the original distribution (uniform distribution could a future extension). Then, subsamples are either – where possible – taken from existing true negative edges or are created using type-based sampling.

In type-based sampling, head and tail node are randomly sampled from a reduced pool of all nodes, which only includes nodes with types that are compatible with the corresponding head- or tail-role of the given relation type. E.g., for the relation type GENE_DRUG, one random node of type GENE is selected as head node and one random node of type DRUG is selected as tail.

In most cases where true negative edges exist, however, their number is smaller than the number of positive examples. In these cases, all true negative samples are used for the negative set, which is then extended by samples created by type-based sampling.

To facilitate model application

• Edges that contain nodes that are not present in the training set are dropped from the test set. This facilitates use of embedding-based models that usually cannot make predictions for nodes that have not been embedded during training.

Avoiding train-test information leakage and trivial predictions in the test set

• Removal of reverse edges If the graph is directed, reverse edges are removed from the training set. The reason for this is that if the original edge a-b was undirected, both directions a→b and a←b are materialized in the directed graph. If one of these directed edges would be present in the training set and one in the test set, the prediction would be trivial. Therefore, in these cases, the reverse edges from the training set are removed. (Note that edges are removed from the training set instead of the test set because this is advantagous for maintaining the train-test-set ratio)
• Removal of super-properties Some types of edges have sub-property characteristics, meaning that relationship x indicates a generic interaction between two entities (e.g. protein_interaction_protein), while relationship y further describes this relationship in more detail (e.g., protein_activation_protein). This means that the presence of x between two nodes does not imply the existence of a relation y between those same entities, but the presence of y necessarily implies the existence of x. These kinds of relationships could cause information leakage in the datasets, therefore super-relations of relations present in the training set are removed from the test set.

As randomly sampled negative edges can produce noise or trivial examples, true negative edges (i.e., relationships that were explicitly mentioned to not exist) were used wherever possible. Specifically, for disease_drug and disease_phenotype edges, true negative examples were extracted from the data source directly, as they were explicitly stated. For gene-anatomy relationships, over-expression and under-expression data was used as contradicting data. For other relationship-types, e.g., gene_activation_gene and drug_inhibition_gene, this indirect true negative sample creation could not be applied, as the relationship does not hold all information necessary (the same substance can have both activating and inhibiting effects, e.g. depending on dosage).

(True neg.: whether the data contains true negative relations; Score: whether the data contains evidence quality scores for filtering relations)

The OpenBioLink benchmark files integrate data or identifiers from these sources. The provenance of data items is captured in the benchmark files, and licensing terms of source databases apply to these data items. Please mind these licensing terms when utilizing or redistributing the benchmark files or derivatives thereof.

All original data in the benchmark files created by the OpenBioLink project (not covered by the licenses of external data sources) are released as CC 0.

We offer the benchmark files as-is and make no representations or warranties of any kind concerning the benchmark files, express, implied, statutory or otherwise, including without limitation warranties of title, merchantability, fitness for a particular purpose, non infringement, or the absence of latent or other defects, accuracy, or the present or absence of errors, whether or not discoverable, all to the greatest extent permissible under applicable law.

To aid with comparing the contents of OpenBioLink with other external knowledge graphs, we created an extensive mapping of relations. It is available as Google sheet here.

@article{10.1093/bioinformatics/btaa274,
    author = {Breit, Anna and Ott, Simon and Agibetov, Asan and Samwald, Matthias},
    title = "{OpenBioLink: a benchmarking framework for large-scale biomedical link prediction}",
    journal = {Bioinformatics},
    volume = {36},
    number = {13},
    pages = {4097-4098},
    year = {2020},
    month = {04},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa274},
    url = {https://doi.org/10.1093/bioinformatics/btaa274},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/36/13/4097/33458979/btaa274.pdf},
}

This project received funding from netidee.