Hetionet

class Hetionet(create_inverse_triples=False, random_state=0, **kwargs)[source]

Bases: pykeen.datasets.base.SingleTabbedDataset

The Hetionet dataset is a large biological network.

In its publication [himmelstein2017], it is demonstrated to be useful for link prediction in drug repositioning and made publicly available through its GitHub repository in several formats. The link prediction algorithm showcased does not rely on embeddings, which leaves room for interesting comparison. One such comparison was made during the master’s thesis of Lingling Xu [xu2019].

For reproducibility, the random_state argument is set by default to 0. For permutation studies, you can change this.

himmelstein2017(1,2)

Himmelstein, D. S., et al (2017). Systematic integration of biomedical knowledge prioritizes drugs for repurposing. ELife, 6.

xu2019

Xu, L (2019) A Comparison of Learned and Engineered Features in Network-Based Drug Repositioning. Master’s Thesis.

Initialize the Hetionet dataset from [himmelstein2017].

Parameters

  • create_inverse_triples (bool) – Should inverse triples be created? Defaults to false.

  • random_state (Union[None, str, Generator]) – The random seed to use in splitting the dataset. Defaults to 0.

  • kwargs – keyword arguments passed to pykeen.datasets.base.SingleTabbedDataset.