PuReD-MCL: a graph-based PubMed document clustering methodology | Zendy

Θεοδόσιος Θεοδοσίου | Zendy; Nikos Darzentas | Zendy; Lefteris Angelis | Zendy; Christos Ouzounis | Zendy

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PuReD-MCL: a graph-based PubMed document clustering methodology

Author(s) -

Θεοδόσιος Θεοδοσίου,

Nikos Darzentas,

Lefteris Angelis,

Christos Ouzounis

Publication year - 2008

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btn318

Subject(s) - computer science , cluster analysis , perl , exploit , graph , source code , document clustering , data mining , information retrieval , graph layout , theoretical computer science , artificial intelligence , programming language , graph drawing , computer security

Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed.

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