NETME: on-the-fly knowledge network construction from biomedical literature

被引：8

作者：

Muscolino, Alessandro ^{[1
]}

Di Maria, Antonio ^{[2
]}

Rapicavoli, Rosaria Valentina ^{[1
]}

Alaimo, Salvatore ^{[2
]}

Bellomo, Lorenzo ^{[4
]}

Billeci, Fabrizio ^{[3
]}

Borzi, Stefano ^{[3
]}

Ferragina, Paolo ^{[4
]}

Ferro, Alfredo ^{[2
]}

Pulvirenti, Alfredo ^{[2
]}

机构：

[1] Univ Catania, Dept Phys & Astron, Catania, Italy

[2] Univ Catania, Dept Clin & Expt Med, Catania, Italy

[3] Univ Catania, Dept Math & Comp Sci, Catania, Italy

[4] Univ Pisa, Dept Comp Sci, Pisa, Italy

来源：

APPLIED NETWORK SCIENCE | 2022年 / 7卷 / 01期

基金：

欧盟地平线“2020”;

关键词：

Network analysis; Knowledge graph; Text mining; CD147; DATABASE; ONTOLOGY; REACTOME; SP1;

D O I：

10.1007/s41109-021-00435-x

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

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.

引用

页数：24

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