Algorithms for clustering data
Algorithms for clustering data
Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems
Validation Measures for Clustering Algorithms Incorporating Biological Information
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Weighted rank aggregation of cluster validation measures
Bioinformatics
An integrative approach for biological data mining and visualisation
International Journal of Data Mining and Bioinformatics
Clustering
An Introduction to Metabolic Networks and Their Structural Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Gene Clustering via Integrated Markov Models Combining Individual and Pairwise Features
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Seeing the forest for the trees
Bioinformatics
Neural network model for integration and visualization of introgressed genome and metabolite data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
A Biologically Inspired Measure for Coexpression Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
How Many Clusters: A Validation Index for Arbitrary-Shaped Clusters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the biological internal connections of such clusters considering common pathways. The proposed measure was tested in two biological databases using three clustering methods.