Elements of information theory
Elements of information theory
Machine Learning - Special issue on learning with probabilistic representations
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
Data Mining and Knowledge Discovery
Preventing "Overfitting" of Cross-Validation Data
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Discovering local structure in gene expression data: the order-preserving submatrix problem
Proceedings of the sixth annual international conference on Computational biology
An iterative strategy for pattern discovery in high-dimensional data sets
Proceedings of the eleventh international conference on Information and knowledge management
VizCluster and its Application on Classifying Gene Expression Data
Distributed and Parallel Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Mining coherent gene clusters from gene-sample-time microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering in Gene Expression Data by Tendency
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Gene Ontology Friendly Biclustering of Expression Profiles
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Interpretable Hierarchical Clustering by Constructing an Unsupervised Decision Tree
IEEE Transactions on Knowledge and Data Engineering
Analysis of SNP-Expression Association Matrices
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
The Journal of Machine Learning Research
Efficient approximation of convex recolorings
Journal of Computer and System Sciences
Classification with feature selection via mathematical programming
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Convex recolorings of strings and trees: Definitions, hardness results and algorithms
Journal of Computer and System Sciences
An optimization-based approach for data classification
Optimization Methods & Software - Systems Analysis, Optimization and Data Mining in Biomedicine
An Evolutionary Approach for Sample-Based Clustering on Microarray Data
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Artificial immune system for classification of gene expression data
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Artificial immune system for classification of cancer
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Enterprise data classification using semantic web technologies
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Effectivity of internal validation techniques for gene clustering
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
On local optima in learning bayesian networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Efficient approximation of convex recolorings
APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques
Convex recolorings of strings and trees: definitions, hardness results and algorithms
WADS'05 Proceedings of the 9th international conference on Algorithms and Data Structures
Analyzing tumor gene expression profiles
Artificial Intelligence in Medicine
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Recent studies (Alizadeh et al, [1]; Bittner et al,[5]; Golub et al, [11]) demonstrate the discovery of putative disease subtypes from gene expression data. The underlying computational problem is to partition the set of sample tissues into statistically meaningful classes. In this paper we present a novel approach to class discovery and develop automatic analysis methods. Our approach is based on statistically scoring candidate partitions according to the overabundance of genes that separate the different classes. Indeed, in biological datasets, an overabundance of genes separating known classes is typically observed. we measure overabundance against a stochastic null model. This allows for highlighting subtle, yet meaningful, partitions that are supported on a small subset of the genes.Using simulated annealing we explore the space of all possible partitions of the set of samples, seeking partitions with statistically significant overabundance of differentially expressed genes. We demonstrate the performance of our methods on synthetic data, where we recover planted partitions. Finally, we turn to tumor expression datasets, and show that we find several highly pronounced partitions.