Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Knowledge and Data Engineering
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
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Advance of high-throughput technologies, such as the microarray and mass spectrometry, has provided an effective approach for the development of systems biology, which aims at understanding the complex functions and properties of biological systems and processes. Revealing the functional correlated genes with co-expression pattern from microarray data allows us to infer the transcriptional regulatory networks and perform functional annotation of genes, and has become one vital step towards the implementation of integrative systems biology. Clustering is particularly useful and preliminary methodology for the discovery of co-expressed genes, for which many conventional clustering algorithms developed in the literature can be potentially useful. However, due to existing large amount of noise and a variety of uncertainties in the microarray data, it is vital important to develop techniques which are robust to noise and effective to incorporate user-specified objectives and preference. For this particular purpose, this paper presented a Genetic Algorithm (GA) based hybrid method for the co-expression gene discovery, which intends to extract the gene groups that have maximal dissimilarity between groups and maximal similarity within a group. The experimental results show that the proposed algorithm is able to extract more meaningful, sensible and significant co-expression gene groups than the traditional clustering methods such as the K-means algorithm. Besides presenting the proposed hybrid GA-based clustering algorithm for co-expression gene discovery, this paper introduces a new framework of integrative systems biology employed in our current research.