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This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using ...
Sep 27, 2006 · The polynomial model based clustering is compared with standard clustering methods under different conditions and applied to a real gene ...
The polynomial model based clustering is compared with standard clustering methods under different conditions and applied to a real gene expression data set. It ...
This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using ...
Improved Robustness in Time Series Analysis of Gene Expression Data by Polynomial Model Based Clustering ... Authors: Michael Hirsch; Allan Tucker; Stephen Swift ...
This paper introduces a method to overcome such problems associated with noise and missing data by modelling the time series data with polynomials and using ...
and Liu X., "Improved Robustness in Time Series Analysis of Gene Expression Data by Polynomial Model Based Clustering", The Proceedings of the second ...
A distinguishing feature of SSC is that it accurately estimates individual gene expression profiles and the mean gene expression profile within clusters ...
Missing: Improved | Show results with:Improved
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In this work we propose a new approach, based on a solid mathematical formalism, able to visualize and cluster noisy gene expression data with missing data ...
Missing: Improved | Show results with:Improved
This paper describes a new technique for clustering short time series of gene expression data. The technique is a generalization of the template-based ...