In the paper a new Multi-Layers approach called Multi-Layers Model MLM) for the analysis of stochastic signals and its application to the analysis of gene expression data is presented. It consists in the generation of sub-samples from the input signal by applying a threshold technique based on cut-set optimal conditions. The MLM has been applied on synthetic and real microarray data for the identification of particular regions across DNA called nucleosomes and linkers. Nucleosomes are the fundamental repeating subunits of all eukaryotic chromatin, and their positioning provides useful information regarding the regulation of gene expression in eukaryotic cells. Results have shown a good recognition rate on synthetic data, moreover, the MLM shows a good agreement with a recently published method based on Hidden Markov Model when tested on the Saccharomyces cerevisiae chromosomes microarray data.
|Titolo della pubblicazione ospite||Lecture Notes in Computer Science, 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12-14, 2007|
|Numero di pagine||8|
|Stato di pubblicazione||Published - 2007|
|Nome||LECTURE NOTES IN COMPUTER SCIENCE|