Ensemble non-negative matrix factorization methods for clustering protein–protein interactions
Author(s) -
Derek Greene,
Gerard Cagney,
Nevan J. Krogan,
Pádraig Cunningham
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/btn286
Subject(s) - cluster analysis , computer science , hierarchical clustering , non negative matrix factorization , hierarchy , proteome , matrix decomposition , computational biology , data mining , theoretical computer science , bioinformatics , artificial intelligence , biology , eigenvalues and eigenvectors , physics , quantum mechanics , economics , market economy
When working with large-scale protein interaction data, an important analysis task is the assignment of pairs of proteins to groups that correspond to higher order assemblies. Previously a common approach to this problem has been to apply standard hierarchical clustering methods to identify such a groups. Here we propose a new algorithm for aggregating a diverse collection of matrix factorizations to produce a more informative clustering, which takes the form of a 'soft' hierarchy of clusters.
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