Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study

Wu, M; Zhang, Y; Zhang, G; Lu, J

Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study

Wu, M

Zhang, Y

Zhang, G

Lu, J

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Publisher:: ELSEVIER SCIENCE INC
Publication Type:: Journal Article
Citation:: Technological Forecasting and Social Change, 2021, 164
Issue Date:: 2021-03-01

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Field	Value	Language
dc.contributor.author	Wu, M https://orcid.org/0000-0003-3956-7808
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.date.accessioned	2022-04-24T21:16:27Z
dc.date.available	2022-04-24T21:16:27Z
dc.date.issued	2021-03-01
dc.identifier.citation	Technological Forecasting and Social Change, 2021, 164
dc.identifier.issn	0040-1625
dc.identifier.issn	1873-5509
dc.identifier.uri	http://hdl.handle.net/10453/156572
dc.description.abstract	Literature-based knowledge (LBD) discovery is a practical approach to inferring the associations between diseases and genetic factors from unstructured biomedical data, i.e., the literature. However, most of the contemporary LBD methods are designed for specific cases and rely heavily on prior knowledge. In this paper, we propose an adaptable and transferable methodology that not only summarizes the genetic factors known to be associated with a queried disease but also predicts likely associations that have yet to be identified. The framework incorporates different biomedical entities in a heterogeneous co-occurrence network. Three centrality indicators, coupled with a novel measure based on intersection ratios, capture the importance and specificity of each factor to the disease under study. Undiscovered, but likely, associations are identified through a semantic similarity matrix generated by our Bioentity2Vec model and an innovative weighted link prediction algorithm. The final outputs are ranked lists of the most relevant known or potential biomedical associations. To both test and showcase the methodology, we conducted a case study on atrial fibrillation. The analysis yields specific insights into the key biomedical entities associated with this disease. Moreover, it demonstrates the kind of valuable decision support this framework can provide to medical researchers, policymakers and public health administrations.
dc.language	English
dc.publisher	ELSEVIER SCIENCE INC
dc.relation	http://purl.org/au-research/grants/arc/DE190100994
dc.relation.ispartof	Technological Forecasting and Social Change
dc.relation.isbasedon	10.1016/j.techfore.2020.120513
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	10 Technology, 14 Economics, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Science Studies
dc.title	Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study
dc.type	Journal Article
utslib.citation.volume	164
utslib.for	10 Technology
utslib.for	14 Economics
utslib.for	15 Commerce, Management, Tourism and Services
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-24T21:16:25Z
pubs.publication-status	Published
pubs.volume	164

Abstract:

Literature-based knowledge (LBD) discovery is a practical approach to inferring the associations between diseases and genetic factors from unstructured biomedical data, i.e., the literature. However, most of the contemporary LBD methods are designed for specific cases and rely heavily on prior knowledge. In this paper, we propose an adaptable and transferable methodology that not only summarizes the genetic factors known to be associated with a queried disease but also predicts likely associations that have yet to be identified. The framework incorporates different biomedical entities in a heterogeneous co-occurrence network. Three centrality indicators, coupled with a novel measure based on intersection ratios, capture the importance and specificity of each factor to the disease under study. Undiscovered, but likely, associations are identified through a semantic similarity matrix generated by our Bioentity2Vec model and an innovative weighted link prediction algorithm. The final outputs are ranked lists of the most relevant known or potential biomedical associations. To both test and showcase the methodology, we conducted a case study on atrial fibrillation. The analysis yields specific insights into the key biomedical entities associated with this disease. Moreover, it demonstrates the kind of valuable decision support this framework can provide to medical researchers, policymakers and public health administrations.

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http://hdl.handle.net/10453/156572