DNA microarrays can provide information about the expression levels of thousands of genes, however these measurements are affected by errors and noise; moreover biological processes develop in very different time scales. A way to cope with these uncertain data is to represent expression level signals in a symbolic way and to adapt sub-string matching algorithms (such as the Longest Common Subsequence) for reconstructing the underlying regulatory network. In this work a first simple task of deciding the regulation direction given a set of correlated genes is studied. As a validation test, the approach is applied to four biological datasets composed of Yeast cell-cycle regulated genes under different synchronization methods.
Coping with Uncertainty in Temporal Gene Expressions Using Symbolic Representations
BADALONI, SILVANA;FALDA, MARCO
2010
Abstract
DNA microarrays can provide information about the expression levels of thousands of genes, however these measurements are affected by errors and noise; moreover biological processes develop in very different time scales. A way to cope with these uncertain data is to represent expression level signals in a symbolic way and to adapt sub-string matching algorithms (such as the Longest Common Subsequence) for reconstructing the underlying regulatory network. In this work a first simple task of deciding the regulation direction given a set of correlated genes is studied. As a validation test, the approach is applied to four biological datasets composed of Yeast cell-cycle regulated genes under different synchronization methods.Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.