Abstract
In this paper, a new feature extracting method and clustering scheme in spectral space for gene expression data was proposed. We model each member of same cluster as the sum of cluster’s representative term and experimental artifacts term. More compact clusters and hence better clustering results can be obtained through extracting essential features or reducing experimental artifacts. In term of the periodicity of gene expression profile data, features extracting is performed in DCT domain by soft-thresholding de-noising method. Clustering process is based on OPTOC competitive learning strategy. The results for clustering real gene expression profiles show that our method is better than directly clustering in the original space.
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Wu, S., Liew, A.W.C., Yan, H. (2005). OPTOC-Based Clustering Analysis of Gene Expression Profiles in Spectral Space. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_113
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DOI: https://doi.org/10.1007/11427469_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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