Clustering Algorithms
Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering short time series gene expression data
Bioinformatics
Clustering of unevenly sampled gene expression time-series data
Fuzzy Sets and Systems
Clustering gene expression series with prior knowledge
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
A new profile alignment method for clustering gene expression data
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Microarray Time-Series Data Clustering via Multiple Alignment of Gene Expression Profiles
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
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Clustering gene expression data given in terms of time-series is a challenging problem that imposes its own particular constraints, namely exchanging two or more time points is not possible as it would deliver quite different results, and also it would lead to erroneous biological conclusions. We have focused on issues related to clustering gene expression temporal profiles, and devised a novel algorithm for clustering gene temporal expression profile microarray data. The proposed clustering method introduces the concept of profile alignment which is achieved by minimizing the area between two aligned profiles. The overall pattern of expression in the time-series context is accomplished by applying agglomerative clustering combined with profile alignment, and finding the optimal number of clusters by means of a variant of a clustering index, which can effectively decide upon the optimal number of clusters for a given dataset. The effectiveness of the proposed approach is demonstrated on two well-known datasets, yeast and serum, and corroborated with a set of pre-clustered yeast genes, which show a very high classification accuracy of the proposed method, though it is an unsupervised scheme.