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STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs

  • MOTIVATION The majority of biomedical knowledge is stored in structured databases or as unstructured text in scientific publications. This vast amount of information has led to numerous machine learning-based biological applications using either text through natural language processing (NLP) or structured data through knowledge graph embedding models (KGEMs). However, representations based on a single modality are inherently limited. RESULTS To generate better representations of biological knowledge, we propose STonKGs, a Sophisticated Transformer trained on biomedical text and Knowledge Graphs (KGs). This multimodal Transformer uses combined input sequences of structured information from KGs and unstructured text data from biomedical literature to learn joint representations in a shared embedding space. First, we pre-trained STonKGs on a knowledge base assembled by the Integrated Network and Dynamical Reasoning Assembler (INDRA) consisting of millions of text-triple pairs extracted from biomedical literature by multiple NLP systems. Then, we benchmarked STonKGs against three baseline models trained on either one of the modalities (i.e., text or KG) across eight different classification tasks, each corresponding to a different biological application. Our results demonstrate that STonKGs outperforms both baselines, especially on the more challenging tasks with respect to the number of classes, improving upon the F1-score of the best baseline by up to 0.084 (i.e., from 0.881 to 0.965). Finally, our pre-trained model as well as the model architecture can be adapted to various other transfer learning applications. AVAILABILITY We make the source code and the Python package of STonKGs available at GitHub (https://github.com/stonkgs/stonkgs) and PyPI (https://pypi.org/project/stonkgs/). The pre-trained STonKGs models and the task-specific classification models are respectively available at https://huggingface.co/stonkgs/stonkgs-150k and https://zenodo.org/communities/stonkgs. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

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Metadaten
Document Type:Article
Language:English
Author:Helena Balabin, Charles Tapley Hoyt, Colin Birkenbihl, Benjamin M. Gyori, John Bachman, Alpha Tom Kodamullil, Paul G. Plöger, Martin Hofmann-Apitius, Daniel Domingo-Fernández
Parent Title (English):Bioinformatics
Volume:38
Issue:6
First Page:1648
Last Page:1656
ISSN:1367-4803
URN:urn:nbn:de:hbz:1044-opus-60728
DOI:https://doi.org/10.1093/bioinformatics/btac001
PMID:https://pubmed.ncbi.nlm.nih.gov/34986221
Publisher:Oxford University Press (OUP)
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Date of first publication:2022/01/05
Copyright:© The Author(s) 2022. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License
Funding:This work was supported by the Fraunhofer Cluster of Excellence ‘Cognitive Internet Technologies’ and the Defense Advanced Research Projects Agency (DARPA) Automating Scientific Knowledge Extraction (ASKE) program under award HR00111990009.
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren
Entry in this database:2022/01/20
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International