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MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition

by 1, 2,3,* and 1,3,*
1
Section of Bioinformatics, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London SW7 2AZ, UK
2
Department of Genetics and Genome Biology, University of Leicester, Leicester LE1 7RH, UK
3
Health Data Research (HDR), London NW1 2BE, UK
*
Authors to whom correspondence should be addressed.
Academic Editors: Reza Salek, Justin Van der Hooft, Simon Rogers and Soha Hassoun
Metabolites 2022, 12(4), 276; https://doi.org/10.3390/metabo12040276
Received: 24 February 2022 / Revised: 15 March 2022 / Accepted: 17 March 2022 / Published: 22 March 2022
(This article belongs to the Special Issue Metabolomics in the Age of Cloud Computing, AI and Machine Learning)
Reviewing the metabolomics literature is becoming increasingly difficult because of the rapid expansion of relevant journal literature. Text-mining technologies are therefore needed to facilitate more efficient literature reviews. Here we contribute a standardised corpus of full-text publications from metabolomics studies and describe the development of two metabolite named entity recognition (NER) methods. These methods are based on Bidirectional Long Short-Term Memory (BiLSTM) networks and each incorporate different transfer learning techniques (for tokenisation and word embedding). Our first model (MetaboListem) follows prior methodology using GloVe word embeddings. Our second model exploits BERT and BioBERT for embedding and is named TABoLiSTM (Transformer-Affixed BiLSTM). The methods are trained on a novel corpus annotated using rule-based methods, and evaluated on manually annotated metabolomics articles. MetaboListem (F1-score 0.890, precision 0.892, recall 0.888) and TABoLiSTM (BioBERT version: F1-score 0.909, precision 0.926, recall 0.893) have achieved state-of-the-art performance on metabolite NER. A training corpus with full-text sentences from >1000 full-text Open Access metabolomics publications with 105,335 annotated metabolites was created, as well as a manually annotated test corpus (19,138 annotations). This work demonstrates that deep learning algorithms are capable of identifying metabolite names accurately and efficiently in text. The proposed corpus and NER algorithms can be used for metabolomics text-mining tasks such as information retrieval, document classification and literature-based discovery and are available from the omicsNLP GitHub repository. View Full-Text
Keywords: deep learning; named entity recognition; natural language processing deep learning; named entity recognition; natural language processing
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MDPI and ACS Style

Yeung, C.S.; Beck, T.; Posma, J.M. MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition. Metabolites 2022, 12, 276. https://doi.org/10.3390/metabo12040276

AMA Style

Yeung CS, Beck T, Posma JM. MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition. Metabolites. 2022; 12(4):276. https://doi.org/10.3390/metabo12040276

Chicago/Turabian Style

Yeung, Cheng S., Tim Beck, and Joram M. Posma. 2022. "MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition" Metabolites 12, no. 4: 276. https://doi.org/10.3390/metabo12040276

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