Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Discovering Patterns and Subfamilies in Biosequences
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustal W and Clustal X version 2.0
Bioinformatics
Ontology-Driven Co-clustering of Gene Expression Data
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
A modular database architecture enabled to comparative sequence analysis
Transactions on large-scale data- and knowledge-centered systems IV
| Hi-index | 0.00 |
Signal finding (pattern discovery) in biological sequences is a fundamental problem in both computer science and molecular biology. Many approaches have been proposed for extracting interesting patterns (or motifs) from DNA/RNA and protein sequences. Some approaches are based on simple and multiple alignment techniques, some use biological knowledge and others do not. In this paper, we propose a de novo framework that performs motifs identification and exploits a constrained co-clustering technique allowing one to simultaneously find associations between groups of protein sequences and groups of motifs. We show that the presented approach is able to group together protein sequences belonging to the same families and, at the same time to provide a set of characterizing motifs.