| Abstrakt: |
Microarray technology provides an opportunity to view transcriptions at genomic level under different experimental conditions. Generally, co-expressed genes, which are members of the same cluster, are expected to have similar function, but unfortunately it is not true due to various reasons including co-expression does not necessarily imply co-regulation. To improve the results of clustering, we investigate a method based on singular value decomposition (SVD) for integrating diverse data sources. We also introduce a new cluster evaluation method based on mutual information. Using time series data sets on yeast, we have empirically demonstrated the effectiveness of SVD as a data integrator. |
