Background: Clustering methods are widely used on gene expression data to categorize genes with similar expression profiles. Finding an appropriate (dis)similarity measure is crit...
Kyungpil Kim, Shibo Zhang, Keni Jiang, Li Cai, In-...
Background: Gene expression data can be analyzed by summarizing groups of individual gene expression profiles based on GO annotation information. The mean expression profile per g...
Background: Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend up...
Helen M. Parsons, Christian Ludwig, Ulrich L. G&uu...
Background: DNA Microarrays have become the standard method for large scale analyses of gene expression and epigenomics. The increasing complexity and inherent noisiness of the ge...
Background: High-throughput functional genomics technologies generate large amount of data with hundreds or thousands of measurements per sample. The number of sample is usually m...
Cheng-Jian Xu, Huub C. J. Hoefsloot, Age K. Smilde