IPCAT '97 Proceedings of the second international workshop on Information processing in cell and tissues
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Using Optimal Dependency-Trees for Combinational Optimization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Analysis of Gene Expression Microarrays for Phenotype Classification
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Genetic Algorithms, Clustering, and the Breaking of Symmetry
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
Biclustering in Gene Expression Data by Tendency
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Biclustering Gene-Feature Matrices for Statistically Significant Dense Patterns
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
GA-based approach to discover meaningful biclusters
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Biclustering of Expression Data Using Simulated Annealing
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Biclustering Models for Structured Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Biclustering of Expression Data with Evolutionary Computation
IEEE Transactions on Knowledge and Data Engineering
Shifting and scaling patterns from gene expression data
Bioinformatics
Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
Possibilistic approach for biclustering microarray data
Computers in Biology and Medicine
A stability index for feature selection
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
The equation for response to selection and its use for prediction
Evolutionary Computation
Random walk biclustering for microarray data
Information Sciences: an International Journal
BiHEA: A Hybrid Evolutionary Approach for Microarray Biclustering
BSB '09 Proceedings of the 4th Brazilian Symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology
An automatic gene ontology software tool for bicluster and cluster comparisons
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Improving stability of feature selection methods
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Bioinformatics
Iterated local search for biclustering of microarray data
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Noise-robust algorithm for identifying functionally associated biclusters from gene expression data
Information Sciences: an International Journal
Combinatorial optimization by learning and simulation of Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Shifting patterns discovery in microarrays with evolutionary algorithms
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
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Background: One of the emerging techniques for performing the analysis of the DNA microarray data known as biclustering is the search of subsets of genes and conditions which are coherently expressed. These subgroups provide clues about the main biological processes. Until now, different approaches to this problem have been proposed. Most of them use the mean squared residue as quality measure but relevant and interesting patterns can not be detected such as shifting, or scaling patterns. Furthermore, recent papers show that there exist new coherence patterns involved in different kinds of cancer and tumors such as inverse relationships between genes which can not be captured. Results: The proposed measure is called Spearman's biclustering measure (SBM) which performs an estimation of the quality of a bicluster based on the non-linear correlation among genes and conditions simultaneously. The search of biclusters is performed by using a evolutionary technique called estimation of distribution algorithms which uses the SBM measure as fitness function. This approach has been examined from different points of view by using artificial and real microarrays. The assessment process has involved the use of quality indexes, a set of bicluster patterns of reference including new patterns and a set of statistical tests. It has been also examined the performance using real microarrays and comparing to different algorithmic approaches such as Bimax, CC, OPSM, Plaid and xMotifs. Conclusions: SBM shows several advantages such as the ability to recognize more complex coherence patterns such as shifting, scaling and inversion and the capability to selectively marginalize genes and conditions depending on the statistical significance.