Multi-way set enumeration in real-valued tensors
Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors
Describing the orthology signal in a PPI network at a functional, complex level
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
LGM: mining frequent subgraphs from linear graphs
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
CLARM: an integrative approach for functional modules discovery
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A Coclustering Approach for Mining Large Protein-Protein Interaction Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Mining from protein–protein interactions
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
| Hi-index | 3.84 |
Motivation: Modern systems biology aims at understanding how the different molecular components of a biological cell interact. Often, cellular functions are performed by complexes consisting of many different proteins. The composition of these complexes may change according to the cellular environment, and one protein may be involved in several different processes. The automatic discovery of functional complexes from protein interaction data is challenging. While previous approaches use approximations to extract dense modules, our approach exactly solves the problem of dense module enumeration. Furthermore, constraints from additional information sources such as gene expression and phenotype data can be integrated, so we can systematically mine for dense modules with interesting profiles. Results: Given a weighted protein interaction network, our method discovers all protein sets that satisfy a user-defined minimum density threshold. We employ a reverse search strategy, which allows us to exploit the density criterion in an efficient way. Our experiments show that the novel approach is feasible and produces biologically meaningful results. In comparative validation studies using yeast data, the method achieved the best overall prediction performance with respect to confirmed complexes. Moreover, by enhancing the yeast network with phenotypic and phylogenetic profiles and the human network with tissue-specific expression data, we identified condition-dependent complex variants. Availability: A C++ implementation of the algorithm is available at http://www.kyb.tuebingen.mpg.de/~georgii/dme.html. Contact: koji.tsuda@tuebingen.mpg.de Supplementary information:Supplementary data are available at Bioinformatics online.