Algorithm Note: PK-means: A new algorithm for gene clustering
Computational Biology and Chemistry
Assessing agreement of clustering methods with gene expression microarray data
Computational Statistics & Data Analysis
Online phenotype discovery based on minimum classification error model
Pattern Recognition
Extending the rand, adjusted rand and jaccard indices to fuzzy partitions
Journal of Intelligent Information Systems
Generalized clustergrams for overlapping biclusters
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
CDVE'10 Proceedings of the 7th international conference on Cooperative design, visualization, and engineering
Comparing fuzzy, probabilistic, and possibilistic partitions
IEEE Transactions on Fuzzy Systems
WF-MSB: A weighted fuzzy-based biclustering method for gene expression data
International Journal of Data Mining and Bioinformatics
A systematic comparison of genome scale clustering algorithms
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Gene clustering using particle swarm optimizer based memetic algorithm
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Improved gene expression clustering with the parameter-free PKNNG metric
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
Computer Methods and Programs in Biomedicine
Engineering Applications of Artificial Intelligence
eXploratory K-Means: A new simple and efficient algorithm for gene clustering
Applied Soft Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust Bayesian Clustering for Replicated Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computational Statistics & Data Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty
The Journal of Machine Learning Research
Subspace clustering of high-dimensional data: an evolutionary approach
Applied Computational Intelligence and Soft Computing
Hi-index | 3.84 |
Motivation: Microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including hierarchical clustering, K-means, PAM, SOM, mixture model-based clustering and tight clustering have been widely used in the literature. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods. Results: In this paper, six gene clustering methods are evaluated by simulated data from a hierarchical log-normal model with various degrees of perturbation as well as four real datasets. A weighted Rand index is proposed for measuring similarity of two clustering results with possible scattered genes (i.e. a set of noise genes not being clustered). Performance of the methods in the real data is assessed by a predictive accuracy analysis through verified gene annotations. Our results show that tight clustering and model-based clustering consistently outperform other clustering methods both in simulated and real data while hierarchical clustering and SOM perform among the worst. Our analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis. Contact: ctseng@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online.