Large-scale extraction of brain connectivity from the neuroscientific literature

Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis...

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Main Authors:	Richardet, Renaud, Chappelier, Jean-Cédric, Telefont, Martin, Hill, Sean
Format:	Online
Language:	English
Published:	Oxford University Press 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426844/

id	pubmed-4426844
recordtype	oai_dc
spelling	pubmed-44268442015-05-15 Large-scale extraction of brain connectivity from the neuroscientific literature Richardet, Renaud Chappelier, Jean-Cédric Telefont, Martin Hill, Sean Original Papers Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity. Oxford University Press 2015-05-15 2015-01-20 /pmc/articles/PMC4426844/ /pubmed/25609795 http://dx.doi.org/10.1093/bioinformatics/btv025 Text en © The Author 2015. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Richardet, Renaud Chappelier, Jean-Cédric Telefont, Martin Hill, Sean
spellingShingle	Richardet, Renaud Chappelier, Jean-Cédric Telefont, Martin Hill, Sean Large-scale extraction of brain connectivity from the neuroscientific literature
author_facet	Richardet, Renaud Chappelier, Jean-Cédric Telefont, Martin Hill, Sean
author_sort	Richardet, Renaud
title	Large-scale extraction of brain connectivity from the neuroscientific literature
title_short	Large-scale extraction of brain connectivity from the neuroscientific literature
title_full	Large-scale extraction of brain connectivity from the neuroscientific literature
title_fullStr	Large-scale extraction of brain connectivity from the neuroscientific literature
title_full_unstemmed	Large-scale extraction of brain connectivity from the neuroscientific literature
title_sort	large-scale extraction of brain connectivity from the neuroscientific literature
description	Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository. Instead, these experimental results are published in natural language, scattered among individual scientific publications. This lack of normalization and centralization hinders the large-scale integration of brain connectivity results. In this article, we present text-mining models to extract and aggregate brain connectivity results from 13.2 million PubMed abstracts and 630 216 full-text publications related to neuroscience. The brain regions are identified with three different named entity recognizers (NERs) and then normalized against two atlases: the Allen Brain Atlas (ABA) and the atlas from the Brain Architecture Management System (BAMS). We then use three different extractors to assess inter-region connectivity.
publisher	Oxford University Press
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4426844/
_version_	1613221683613663232

Large-scale extraction of brain connectivity from the neuroscientific literature

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