Universal dynamical properties preclude standard clustering in a large class of biochemical data
Author(s) -
Florian Gomez,
R. Stoop,
Ruedi Stoop
Publication year - 2014
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/btu332
Subject(s) - cluster analysis , computer science , space (punctuation) , mechanism (biology) , feature (linguistics) , dynamical systems theory , variety (cybernetics) , class (philosophy) , dimension (graph theory) , feature vector , data mining , theoretical computer science , statistical physics , biological system , artificial intelligence , mathematics , physics , biology , linguistics , philosophy , quantum mechanics , pure mathematics , operating system
Clustering of chemical and biochemical data based on observed features is a central cognitive step in the analysis of chemical substances, in particular in combinatorial chemistry, or of complex biochemical reaction networks. Often, for reasons unknown to the researcher, this step produces disappointing results. Once the sources of the problem are known, improved clustering methods might revitalize the statistical approach of compound and reaction search and analysis. Here, we present a generic mechanism that may be at the origin of many clustering difficulties.
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