Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
On the red-blue set cover problem
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
On the hardness of approximating label-cover
Information Processing Letters
Approximation algorithms for the Label-CoverMAX and Red-Blue Set Cover problems
Journal of Discrete Algorithms
IEEE Transactions on Knowledge and Data Engineering
The Hardness of Approximating Spanner Problems
Theory of Computing Systems
Interaction-site prediction for protein complexes
Bioinformatics
Bioinformatics
Succinct summarization of transactional databases: an overlapped hyperrectangle scheme
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
One in a million: picking the right patterns
Knowledge and Information Systems
Krimp: mining itemsets that compress
Data Mining and Knowledge Discovery
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Correlated motif covering (CMC) is the problem of finding a set of motif pairs, i.e., pairs of patterns, in the sequences of proteins from a protein-protein interaction network (PPI-network) that describe the interactions in the network as concisely as possible. In other words, a perfect solution for CMC would be a minimal set of motif pairs that describes the interaction behavior perfectly in the sense that two proteins from the network interact if and only if their sequences match a motif pair in the minimal set. In this paper, we introduce and formally define CMC and show that it is closely related to the red-blue set cover (RBSC) problem and its weighted version (WRBSC)—both well-known NP-hard problems for that there exist several algorithms with known approximation factor guarantees. We prove the hardness of approximation of CMC by providing an approximation factor preserving reduction from RBSC to CMC. We show the existence of a theoretical approximation algorithm for CMC by providing an approximation factor preserving reduction from CMC to WRBSC. We adapt the latter algorithm into a functional heuristic for CMC, called CMC-approx, and experimentally assess its performance and biological relevance. The implementation in Java can be found at http://bioinformatics.uhasselt.be.