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Literature DB >> 31822126

Working the literature harder: what can text mining and bibliometric analysis reveal?

Yu Han^1,2, Sara A Wennersten^1,2, Maggie P Y Lam^1,2,3.

Abstract

Entities: Chemical Disease Gene Species

Keywords: Bibliometrics; human disease; molecular interaction; protein function; text-mining

Mesh：

Year: 2019 PMID： 31822126 PMCID： PMC7033621 DOI： 10.1080/14789450.2019.1703678

Source DB: PubMed Journal: Expert Rev Proteomics ISSN： 1478-9450 Impact factor: 3.940

× No keyword cloud information.

15 in total

1. The applied value of public investments in biomedical research.

Authors: Danielle Li; Pierre Azoulay; Bhaven N Sampat
Journal: Science Date: 2017-03-30 Impact factor: 47.728

2. Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity.

Authors: Edward Lau; Vidya Venkatraman; Cody T Thomas; Joseph C Wu; Jennifer E Van Eyk; Maggie P Y Lam
Journal: J Proteome Res Date: 2018-10-09 Impact factor: 4.466

3. Literature-based discovery of new candidates for drug repurposing.

Authors: Hsih-Te Yang; Jiun-Huang Ju; Yue-Ting Wong; Ilya Shmulevich; Jung-Hsien Chiang
Journal: Brief Bioinform Date: 2017-05-01 Impact factor: 11.622

Review 4. Science of science.

Authors: Santo Fortunato; Carl T Bergstrom; Katy Börner; James A Evans; Dirk Helbing; Staša Milojević; Alexander M Petersen; Filippo Radicchi; Roberta Sinatra; Brian Uzzi; Alessandro Vespignani; Ludo Waltman; Dashun Wang; Albert-László Barabási
Journal: Science Date: 2018-03-02 Impact factor: 47.728

5. A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.

Authors: David Westergaard; Hans-Henrik Stærfeldt; Christian Tønsberg; Lars Juhl Jensen; Søren Brunak
Journal: PLoS Comput Biol Date: 2018-02-15 Impact factor: 4.475

6. Geneshot: search engine for ranking genes from arbitrary text queries.

Authors: Alexander Lachmann; Brian M Schilder; Megan L Wojciechowicz; Denis Torre; Maxim V Kuleshov; Alexandra B Keenan; Avi Ma'ayan
Journal: Nucleic Acids Res Date: 2019-07-02 Impact factor: 19.160

7. Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.

Authors: Robert Hoehndorf; Paul N Schofield; Georgios V Gkoutos
Journal: Sci Rep Date: 2015-06-08 Impact factor: 4.379

8. PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more.

Authors: Yifeng Liu; Yongjie Liang; David Wishart
Journal: Nucleic Acids Res Date: 2015-04-29 Impact factor: 16.971

Working the literature harder: what can text mining and bibliometric analysis reveal?

1. The applied value of public investments in biomedical research.

2. Identifying High-Priority Proteins Across the Human Diseasome Using Semantic Similarity.

3. Literature-based discovery of new candidates for drug repurposing.

Review 4. Science of science.

5. A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.

6. Geneshot: search engine for ranking genes from arbitrary text queries.

7. Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.

8. PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more.

9. Bibliometric Analyses Reveal Patterns of Collaboration between ASMS Members.

10. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.

1. Research on primary Sjögren's syndrome in 2004-2021: a Web of Science-based cross-sectional bibliometric analysis.

2. New insight into the current study of high tibial osteotomy: A bibliometric analysis.