TY  - JOUR
U1  - Wissenschaftlicher Artikel
A1  - Balabin, Helena
A1  - Hoyt, Charles Tapley
A1  - Birkenbihl, Colin
A1  - Gyori, Benjamin M.
A1  - Bachman, John
A1  - Tom Kodamullil, Alpha
A1  - Plöger, Paul G.
A1  - Hofmann-Apitius, Martin
A1  - Domingo-Fernández, Daniel
T1  - STonKGs: A Sophisticated Transformer Trained on Biomedical Text and Knowledge Graphs
JF  - Bioinformatics
N2  - 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.
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-60728
SN  - 1367-4803
SS  - 1367-4803
U6  - https://doi.org/10.1093/bioinformatics/btac001
DO  - https://doi.org/10.1093/bioinformatics/btac001
PM  - 34986221
VL  - 38
IS  - 6
SP  - 1648
EP  - 1656
PB  - Oxford University Press (OUP)
ER  -