Abstract
Background: Chemical compounds and proteins/genes are an important class of entities in
biomedical research, and their interactions play a key role in precision medicine, drug discovery, basic
clinical research, and building knowledge bases. Many computational methods have been proposed to
identify chemical–protein interactions. However, the majority of these proposed models cannot model
long-distance dependencies between chemical and protein, and the neural networks used to suffer from
gradient descent, with little taking into account the characteristics of the chemical structure characteristics
of the compound.
Methods: To address the above limitations, we propose a novel model, SIMEON, to identify chemical–
protein interactions. First, an input sequence is represented with pre-trained language model and an attention
mechanism is used to uncover contribution degree of different words to entity relations and potential
semantic information. Secondly, key features are extracted by a multi-layer stacked Bidirectional
Gated Recurrent Units (Bi-GRU)-normalization residual network module to resolve higherorder
dependencies while overcoming network degradation. Finally, the representation is introduced to
be enhanced by external knowledge regarding the chemical structure characteristics of the compound
external knowledge.
Results: Excellent experimental results show that our stacked integration model combines the advantages
of Bi-GRU, normalization methods, and external knowledge to improve the performance of
the model by complementing each other.
Conclusion: Our proposed model shows good performance in chemical-protein interaction extraction,
and it can be used as a useful complement to biological experiments to identify chemical-protein interactions.
Keywords:
Chemical–protein interaction, normalization methods, stacked integration model, Bi-GRU, molecular and protein representation, biomedical text.
Graphical Abstract
[3]
Krallinger M, Rabal O, Akhondi SA, et al. Overview of the BioCreative VI chemical-protein interaction track. Proceedings of the sixth BioCreative challenge evaluation workshop. Bethesda, MD, USA. 2017; pp. 141-6.
[8]
Pei-Yau L, Zhe H, Tingting Z, et al. Extracting chemical–protein interactions from literature using sentence structure analysis and feature engineering. Database (Oxford) 2019; 1-8.
[15]
Defferrard M, Bresson X, Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. In: 30th Conference on Neural Information Processing Systems (NIPS 2016); Barcelona, Spain. 2016.
[17]
Mehryary F, Björne J, Salakoski T, et al. Combining support vector machines and LSTM networks for chemical–protein relation extraction. Proceedings of the BioCreative VI Workshop. 1: pp. 176-80.
[22]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 07-12 June 2015; Boston, MA: IEEE 2015.
[24]
Peters M, Neumann M, Iyyer M, et al. Deep contextualized word representations. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics. In: Human language Technologies; 20018; pp. 2227-.
[26]
Devlin J, Chang M, Lee K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019; pp. 4171-.
[32]
Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. 3rd International Conference on Learning Representations, ICLR 2015. San Diego, United States. 2015.
[34]
Shaw P, Uszkoreit J, Vaswani A. Self-attention with relative position representations. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2018; pp. 464-8.
[35]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016; pp. 770-778.
[36]
Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the 32nd International Conference on Machine Learning, PMLR 37. pp. 448-456.2015;
[37]
Xu J, Sun X, Zhang Z, et al. Understanding and improving layer normalization. Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2015; pp. 4381-.
[38]
Nam H, Kim H. Batch-instance normalization for adaptively style-invariant neural networks. Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018; 2563-72.
[39]
Wu Y, He K. Group normalization. Proceedings of the European Conference on Computer Vision (ECCV). 2020; 128(3): 742-55.
[44]
Chowdhury MFM, Lavelli A. FBK-irst: A multi-phase kernel based approach for drug-drug interaction detection and classification that exploits linguistic information. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 351–355. Atlanta, Georgia, USA.
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Yang Lu
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