TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery

Abstract Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine c...

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Autores principales:	Guillermo Serrano Nájera, David Narganes Carlón, Daniel J. Crowther
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5772d4e7752d418392e61c47ac3c588d
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spelling	oai:doaj.org-article:5772d4e7752d418392e61c47ac3c588d2021-12-02T16:35:27ZTrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery10.1038/s41598-021-94897-92045-2322https://doaj.org/article/5772d4e7752d418392e61c47ac3c588d2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94897-9https://doaj.org/toc/2045-2322Abstract Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.Guillermo Serrano NájeraDavid Narganes CarlónDaniel J. CrowtherNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Guillermo Serrano Nájera David Narganes Carlón Daniel J. Crowther TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
description	Abstract Target identification and prioritisation are prominent first steps in modern drug discovery. Traditionally, individual scientists have used their expertise to manually interpret scientific literature and prioritise opportunities. However, increasing publication rates and the wider routine coverage of human genes by omic-scale research make it difficult to maintain meaningful overviews from which to identify promising new trends. Here we propose an automated yet flexible pipeline that identifies trends in the scientific corpus which align with the specific interests of a researcher and facilitate an initial prioritisation of opportunities. Using a procedure based on co-citation networks and machine learning, genes and diseases are first parsed from PubMed articles using a novel named entity recognition system together with publication date and supporting information. Then recurrent neural networks are trained to predict the publication dynamics of all human genes. For a user-defined therapeutic focus, genes generating more publications or citations are identified as high-interest targets. We also used topic detection routines to help understand why a gene is trendy and implement a system to propose the most prominent review articles for a potential target. This TrendyGenes pipeline detects emerging targets and pathways and provides a new way to explore the literature for individual researchers, pharmaceutical companies and funding agencies.
format	article
author	Guillermo Serrano Nájera David Narganes Carlón Daniel J. Crowther
author_facet	Guillermo Serrano Nájera David Narganes Carlón Daniel J. Crowther
author_sort	Guillermo Serrano Nájera
title	TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
title_short	TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
title_full	TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
title_fullStr	TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
title_full_unstemmed	TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery
title_sort	trendygenes, a computational pipeline for the detection of literature trends in academia and drug discovery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5772d4e7752d418392e61c47ac3c588d
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TrendyGenes, a computational pipeline for the detection of literature trends in academia and drug discovery

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