Author:
Muscolino Alessandro,Di Maria Antonio,Rapicavoli Rosaria Valentina,Alaimo Salvatore,Bellomo Lorenzo,Billeci Fabrizio,Borzì Stefano,Ferragina Paolo,Ferro Alfredo,Pulvirenti Alfredo
Abstract
Abstract
Background
The rapidly increasing biological literature is a key resource to automatically extract and gain knowledge concerning biological elements and their relations. Knowledge Networks are helpful tools in the context of biological knowledge discovery and modeling.
Results
We introduce a novel system called NETME, which, starting from a set of full-texts obtained from PubMed, through an easy-to-use web interface, interactively extracts biological elements from ontological databases and then synthesizes a network inferring relations among such elements. The results clearly show that our tool is capable of inferring comprehensive and reliable biological networks.
Funder
Ministero dell’Istruzione, dell’Università e della Ricerca
Regione Siciliana
Horizon 2020 Framework Programme
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Computer Networks and Communications,Multidisciplinary
Cited by
7 articles.
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