Altmetric

MetaboListem and TABoLiSTM: two deep learning algorithms for metabolite named entity recognition

File Description SizeFormat 
metabolites-12-00276.pdfPublished version4.17 MBAdobe PDFView/Open
Title: MetaboListem and TABoLiSTM: two deep learning algorithms for metabolite named entity recognition
Authors: Yeung, C
Beck, T
Posma, JM
Item Type: Journal Article
Abstract: 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 $>$1,000 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. They are available from https://github.com/omicsNLP/MetaboliteNER.
Editors: Salek, R
Van der Hooft, J
Hassoun, S
Rogers, S
Issue Date: 22-Mar-2022
Date of Acceptance: 17-Mar-2022
URI: http://hdl.handle.net/10044/1/96041
DOI: 10.3390/metabo12040276
ISSN: 2218-1989
Publisher: MDPI AG
Start Page: 1
End Page: 23
Journal / Book Title: Metabolites
Volume: 12
Issue: 4
Copyright Statement: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Sponsor/Funder: Medical Research Council (MRC)
Medical Research Council
Funder's Grant Number: MR/S004033/1
MR/S004033/1
Keywords: deep learning
named entity recognition
natural language processing
0301 Analytical Chemistry
0601 Biochemistry and Cell Biology
1103 Clinical Sciences
Publication Status: Published
Open Access location: https://doi.org/10.1101/2022.02.22.481457
Article Number: 276
Online Publication Date: 2022-03-22
Appears in Collections:Department of Metabolism, Digestion and Reproduction
Faculty of Medicine



This item is licensed under a Creative Commons License Creative Commons