Improving the learning of chemical-protein interactions from literature using transfer learning and specialized word embeddings

Database : the Journal of Biological Databases and Curation
P Corbett, J Boyle

Abstract

In this paper, we explore the application of artificial neural network ('deep learning') methods to the problem of detecting chemical-protein interactions in PubMed abstracts. We present here a system using multiple Long Short Term Memory layers to analyse candidate interactions, to determine whether there is a relation and which type. A particular feature of our system is the use of unlabelled data, both to pre-train word embeddings and also pre-train LSTM layers in the neural network. On the BioCreative VI CHEMPROT test corpus, our system achieves an F score of 61.51% (56.10% precision, 67.84% recall).

References

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Citations

Jan 10, 2019·Database : the Journal of Biological Databases and Curation·Pei-Yau LungJinfeng Zhang
Apr 6, 2019·Journal of Morphology·Yuliya KulyominaDuncan J Irschick
Apr 25, 2019·Anais Da Academia Brasileira De Ciências·Gabriel S MasseliIgor L Kaefer
Feb 1, 2020·The Anatomical Record : Advances in Integrative Anatomy and Evolutionary Biology·Rebeca S KhouriDaniel S Fernandes
Oct 28, 2019·The Journal of Animal Ecology·Shai MeiriRichard Shine
Oct 18, 2019·Database : the Journal of Biological Databases and Curation·Rui Antunes, Sérgio Matos
Sep 21, 2019·Protein and Peptide Letters·Cheng ShiHeng Zheng
Dec 14, 2018·Journal of Cheminformatics·Peter Corbett, John Boyle
May 28, 2019·Database : the Journal of Biological Databases and Curation·Yijia ZhangYuanyuan Sun
Feb 12, 2021·Database : the Journal of Biological Databases and Curation·Yifan ShaoGuodong Zhou
Feb 19, 2020·Journal of Biomedical Informatics·Cong SunJian Wang
Nov 14, 2020·ACS Sensors·Feiyun CuiH Susan Zhou

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