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Context-specific interaction networks from vector representation of words

Nature Machine Intelligence volume 1, pages 181–190 (2019)Cite this article

Subjects

A preprint version of the article is available at arXiv.

Abstract

The number of biomedical publications has grown steadily in recent years. However, most biomedical facts are not readily available, but buried in the form of unstructured text. Here we present INtERAcT, an unsupervised method to extract interactions from a corpus of biomedical articles. INtERAcT exploits a vector representation of words, computed on a corpus of domain-specific knowledge, and implements a new metric that estimates an interaction score between two molecules in the space where the corresponding words are embedded. We use INtERAcT to reconstruct the molecular pathways of 10 different cancer types using corpora of disease-specific articles, considering the STRING database as a benchmark. Our metric outperforms currently adopted approaches and it is highly robust to parameter choices, leading to the identification of known molecular interactions in all studied cancer types. Furthermore, our approach does not require text annotation, manual curation or the definition of semantic rules based on expert knowledge, and can therefore be efficiently applied to different scientific domains.

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Fig. 1: Description of the skip-gram model.
Fig. 2: Schematic representation of INtERAcT.
Fig. 3: Performance of INtERAcT on prostate cancer embedding.
Fig. 4: INtERAcT performance compared to other distance measures using STRING as a ground truth.
Fig. 5: Exploration of the influence of word embedding parameters on AUC for different methods and ground truths.
Fig. 6: Parametric dependency of INtERAcT using STRING combined score as a ground truth.

Data availability

The article abstracts and full texts used to generate INtERAcT PPI scores can be freely downloaded from PMC. For this project, STRING interaction scores were downloaded on 19 October 2017. STRING historical data can be downloaded from https://string-db.org/cgi/access.pl?footer_active_subpage=archive. The article collection as well historical STRING data are also available from the corresponding author upon request. The networks generated for KEGG cancer pathways and the corresponding word vectors and entities lists can be downloaded from https://ibm.biz/interact-data.

Code availability

The INtERAcT implementation used to produce the results in this Article can be accessed via https://doi.org/10.5281/zenodo.2576762 (ref. 44) and the open source code is available on GitHub (https://github.com/drugilsberg/interact). The INtERAcT python package can be installed via pip and provides a set of utilities to build interaction networks from word vectors using INtERAcT as well as using other metrics.

INtERAcT is also available as a service hosted on IBM Cloud at https://ibm.biz/interact-aas. The web service builds a molecular interaction network given word vectors in Word2Vec binary format and a list of molecular entities (example data are made available through a download link in the app).

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Acknowledgements

The authors thank C. Bekas and Y. Ineichen for useful discussions. The project leading to this application received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 668858.

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Author notes

  1. Roland Mathis

    Present address: Telepathy Labs, Zurich, Switzerland

  2. These authors contributed equally: Matteo Manica, Roland Mathis, Joris Cadow.

Authors and Affiliations

  1. IBM Research, Zürich, Switzerland

    Matteo Manica, Roland Mathis, Joris Cadow & María Rodríguez Martínez

  2. ETH, Zürich, Switzerland

    Matteo Manica

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Contributions

M.M., R.M., J.C. and M.R.M. conceived the study and analyses. M.M., R.M. and J.C. implemented INtERAcT and performed data analysis. M.R.M. provided biological interpretation. M.M., R.M., J.C. and M.R.M. wrote the manuscript, with input from all authors.

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Correspondence to María Rodríguez Martínez.

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Manica, M., Mathis, R., Cadow, J. et al. Context-specific interaction networks from vector representation of words. Nat Mach Intell 1, 181–190 (2019). https://doi.org/10.1038/s42256-019-0036-1

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