Physiol. Genomics Journal of Neurophysiology
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Physiol. Genomics (February 8, 2005). doi:10.1152/physiolgenomics.00307.2004
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Submitted on December 22, 2004
Accepted on February 3, 2005

Application of Machine Learning and Visualization of Heterogeneous Datasets to Uncover Relationships between Translation and Developmental Stage Expression of C. elegans mRNAs

Marjan Trutschl1, Tzvetanka D Dinkova2, and Robert E Rhoads3*

1 Department of Computer Science, Louisiana State University Health Sciences Center, Shreveport, Louisiana, USA; Center for Bioinformatics and Computational Biology, Louisiana State University Health Sciences Center, Shreveport, Louisiana, USA
2 Department of Biochemistry and Molecular Biology, Louisiana State University Health Sciences Center, Shreveport, Louisiana, USA; Departamento de Bioquimica, Facultad de Quimica, Universidad Nacional Autonoma de Mexico, Mexico, Mexico
3 Department of Biochemistry and Molecular Biology, Louisiana State University Health Sciences Center, Shreveport, Louisiana, USA

* To whom correspondence should be addressed. E-mail: rrhoad{at}lsuhsc.edu.

The relationships between genes in neighboring clusters in a self-organizing map (SOM) and properties attributed to them are sometimes difficult to discern, especially when heterogeneous datasets are used. We report a novel approach to identify correlations between heterogeneous datasets. One dataset, derived from microarray analysis of polysomal distribution, contained changes in the translational efficiency of C. elegans mRNAs resulting from loss of specific eIF4E isoform. The other dataset contained expression patterns of mRNAs across all developmental stages. Two algorithms were applied to these datasets: a classical scatter plot and a SOM. The outputs were linked using a 2D color scale. This revealed that an mRNA's eIF4E-dependent translational efficiency is strongly dependent on its expression during development. This correlation was not detectable with a traditional 1D color scale.




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