An Analysis of Research Themes in the CBR Conference Literature
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Enhanced clustering of biomedical documents using ensemble non-negative matrix factorization
Information Sciences: an International Journal
Algorithms for nonnegative matrix factorization with the β-divergence
Neural Computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Variational Bayesian inference for the Latent Position Cluster Model for network data
Computational Statistics & Data Analysis
Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Expert Systems with Applications: An International Journal
| Hi-index | 3.84 |
Motivation: 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. Results: We apply the proposed Ensemble non-negative matrix factorization (NMF) algorithm to a high-quality assembly of binary protein interactions derived from two proteome-wide studies in yeast. Our experimental evaluation demonstrates that the algorithm lends itself to discovering small localized structures in this data, which correspond to known functional groupings of complexes. In addition, we show that the algorithm also supports the assignment of putative functions for previously uncharacterized proteins, for instance the protein YNR024W, which may be an uncharacterized component of the exosome. Contact: derek.greene@ucd.ie Supplementary information: Supplementary data are available at http://mlg.ucd.ie/nmf.