Biocomputing and Media Research Lab

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MACE

Microarray data analysis and Compression

Description

The problem of microarray data analysis has long been one of the central foci of Bioinformatics research. Simultaneously, the widespread adoption of microarray technology coupled with the large volume of image-based data generated per experiment underlines the importance of developing low-level signal analysis techniques that focus on technical problems specific to microarrays.

Within the above framework, the MACE project incorporates three investigative themes. First, we are developing techniques for lossless compression of the primary (image-based) data from microarray experiments. As part of this thrust we have developed a simple and practical compression technique that resolves a key problem, namely the dependence of compression methods on the complex and error-prone step of spot detection. Second, we are developing method based on results from linear and non-linear algebra for imputation of missing values in microarray data sets. Finally, we are developing techniques that aid in analysis of microarray data, with a special focus on aiding hypotheses formulation and assimilation.

Access to many of the microarray images used in our experiments were provided by the Stanford Microarray Database. This research has been funded in part by the National Science Foundation through the grant IIS-0644418.

Publications

  1. A. Sasho, S. Zhu, and R. Singh, "Identification and Analysis of Cell Cycle Phase Genes by Clustering In Correspondence Subspaces", International Conference on Advances in Computing and Communications, Springer, 2011 (To Appear).
  2. R Bierman and R. Singh, "Influence of Dictionary Size on the Lossless Compression of Microarray Images", IEEE International Symposium on Computer-Based Medical Systems (CBMS), pp. 237-242, 2007. [PDF]
  3. R. Bierman, N. Maniyar, C. Parson, and R. Singh, MACE: Lossless Compression and Analysis of Microarray Images, ACM Symposium on Applied Computing (SAC), pp. 167-172, 2006. [PDF]
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