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McSyBi specifications

Information


Unique identifier OMICS_05220
Name McSyBi
Interface Web user interface
Restrictions to use None
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Yasunori Yamamoto

Publication for McSyBi

McSyBi citations

 (3)
library_books

Biotea: RDFizing PubMed Central in support for the paper as an interface to the Web of Data

2013
J Biomed Semantics
PMCID: 3804025
PMID: 23734622
DOI: 10.1186/2041-1480-4-S1-S5

[…] ernative search mechanism for the Medline database []; articles are initially grouped by means of a keyword-based algorithm and then ordered following a similar algorithm based on sentence alignment. McSyBi [] utilizes clustering techniques in order to make sub-topics explicit from a set of citation data retrieved from PubMed; clusters are created based on information from the title and the abstra […]

library_books

PubMed and beyond: a survey of web tools for searching biomedical literature

2011
PMCID: 3025693
PMID: 21245076
DOI: 10.1093/database/baq036

[…] display all articles in that category. To find a article by multiple categories, one can follow the categories progressively (e.g. first restricting results by year of publication, then by journals).McSyBi () presents clustered results in two distinct fashions: hierarchical or non-hierarchical. While the former provides an overview of the search results, the latter shows relationships among the s […]

library_books

Linking genes to literature: text mining, information extraction, and retrieval applications for biology

2008
Genome Biol
PMCID: 2559992
PMID: 18834499
DOI: 10.1186/gb-2008-9-s2-s8

[…] ts containing this term (document frequency, using global, or within-collection information) [].Document clustering approaches using document similarity calculations have been used by PubClust [] and McSyBi [] to structure further the collection of articles retrieved by keyword searches. A recurrent challenge in both bioinformatics and text processing is the classification of a collection of items […]


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McSyBi institution(s)
Department of Computational Biology, University of Tokyo, Kibanto, Kashiwa, Chiba, Japan

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