Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
ACM Computing Surveys (CSUR)
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A Statistical Method for Finding Transcription Factor Binding Sites
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
A Simple Hyper-Geometric Approach for Discovering Putative Transcription Factor Binding Sites
WABI '01 Proceedings of the First International Workshop on Algorithms in Bioinformatics
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Motif discovery through predictive modeling of gene regulation
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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In modern biology, we had an explosion of genomic data from multiple sources, like measurements of RNA levels, gene sequences, annotations or interaction data. These heterogeneous data provide important information that should be integrated through suitable learning methods aimed at elucidating regulatory networks. We propose an iterative relational clustering procedure for finding modules of co-regulated genes. This approach integrates information concerning known Transcription Factors (TFs)gene interactions with gene expression data to find clusters of genes that share a common regulatory program. The results obtained on two well-known gene expression data sets from Saccharomyces cerevisiae are shown.