Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts

For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-s...

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Autores principales:	Prashant Srivastava, Saptarshi Bej, Kristina Yordanova, Olaf Wolkenhauer
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	text mining self-attention models biological literature mining relationship extraction natural language processing Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/530bc149d682463b988566636474504f
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spelling	oai:doaj.org-article:530bc149d682463b988566636474504f2021-11-25T16:52:34ZSelf-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts10.3390/biom111115912218-273Xhttps://doaj.org/article/530bc149d682463b988566636474504f2021-10-01T00:00:00Zhttps://www.mdpi.com/2218-273X/11/11/1591https://doaj.org/toc/2218-273XFor any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-scale molecular interaction maps and database curation. Text mining and Natural-Language-Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and Machine-Learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention-based models, a special type of Neural-Network (NN)-based architecture that has recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at the sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conducted a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.Prashant SrivastavaSaptarshi BejKristina YordanovaOlaf WolkenhauerMDPI AGarticletext miningself-attention modelsbiological literature miningrelationship extractionnatural language processingMicrobiologyQR1-502ENBiomolecules, Vol 11, Iss 1591, p 1591 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	text mining self-attention models biological literature mining relationship extraction natural language processing Microbiology QR1-502
spellingShingle	text mining self-attention models biological literature mining relationship extraction natural language processing Microbiology QR1-502 Prashant Srivastava Saptarshi Bej Kristina Yordanova Olaf Wolkenhauer Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts
description	For any molecule, network, or process of interest, keeping up with new publications on these is becoming increasingly difficult. For many cellular processes, the amount molecules and their interactions that need to be considered can be very large. Automated mining of publications can support large-scale molecular interaction maps and database curation. Text mining and Natural-Language-Processing (NLP)-based techniques are finding their applications in mining the biological literature, handling problems such as Named Entity Recognition (NER) and Relationship Extraction (RE). Both rule-based and Machine-Learning (ML)-based NLP approaches have been popular in this context, with multiple research and review articles examining the scope of such models in Biological Literature Mining (BLM). In this review article, we explore self-attention-based models, a special type of Neural-Network (NN)-based architecture that has recently revitalized the field of NLP, applied to biological texts. We cover self-attention models operating either at the sentence level or an abstract level, in the context of molecular interaction extraction, published from 2019 onwards. We conducted a comparative study of the models in terms of their architecture. Moreover, we also discuss some limitations in the field of BLM that identifies opportunities for the extraction of molecular interactions from biological text.
format	article
author	Prashant Srivastava Saptarshi Bej Kristina Yordanova Olaf Wolkenhauer
author_facet	Prashant Srivastava Saptarshi Bej Kristina Yordanova Olaf Wolkenhauer
author_sort	Prashant Srivastava
title	Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts
title_short	Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts
title_full	Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts
title_fullStr	Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts
title_full_unstemmed	Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts
title_sort	self-attention-based models for the extraction of molecular interactions from biological texts
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/530bc149d682463b988566636474504f
work_keys_str_mv	AT prashantsrivastava selfattentionbasedmodelsfortheextractionofmolecularinteractionsfrombiologicaltexts AT saptarshibej selfattentionbasedmodelsfortheextractionofmolecularinteractionsfrombiologicaltexts AT kristinayordanova selfattentionbasedmodelsfortheextractionofmolecularinteractionsfrombiologicaltexts AT olafwolkenhauer selfattentionbasedmodelsfortheextractionofmolecularinteractionsfrombiologicaltexts
_version_	1718412894431870976

Self-Attention-Based Models for the Extraction of Molecular Interactions from Biological Texts

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