A massively parallel architecture for a self-organizing neural pattern recognition machine
Computer Vision, Graphics, and Image Processing
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
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
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
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
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
Computers and Operations Research
ACM Transactions on Knowledge Discovery from Data (TKDD)
Fuzzy-Adaptive-Subspace-Iteration-Based Two-Way Clustering of Microarray Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Innovations in Fuzzy Clustering: Theory and Applications
Innovations in Fuzzy Clustering: Theory and Applications
Survey of clustering algorithms
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Finding gene coherent patterns using PATSUB+
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Clustering has been used extensively in the analysis of high-throughput messenger RNA (mRNA) expression profiling with microarrays. Furthermore, clustering has proven elemental in microRNA expression profiling, which demonstrates enormous promise in the areas of cancer diagnosis and treatment, gene function identification, therapy development and drug testing, and genetic regulatory network inference. However, such a practice is inherently limited due to the existence of many uncorrelated genes with respect to sample or condition clustering, or many unrelated samples or conditions with respect to gene clustering. Biclustering offers a solution to such problems by performing simultaneous clustering on both dimensions, or automatically integrating feature selection to clustering without any prior information, so that the relations of clusters of genes (generally, features) and clusters of samples or conditions (data objects) are established. However, the NP-complete computational complexity raises a great challenge to computational methods for identifying such local relations. Here, we propose and demonstrate that a neural-based classifier, ARTMAP, can be modified to perform biclustering in an efficient way, leading to a biclustering algorithm called Biclustering ARTMAP (BARTMAP). Experimental results on multiple human cancer data sets show that BARTMAP can achieve clustering structures with higher qualities than those achieved with other commonly used biclustering or clustering algorithms, and with fast run times.