Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Protein Secondary-Structure Modeling with Probabilistic Networks
Proceedings of the 1st International Conference on Intelligent Systems for Molecular Biology
IEEE Transactions on Information Theory
A One Class Classifier for Signal Identification: A Biological Case Study
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
A Fuzzy One Class Classifier for Multi Layer Model
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Interval Length Analysis in Multi Layer Model
Computational Intelligence Methods for Bioinformatics and Biostatistics
A one class KNN for signal identification: a biological case study
International Journal of Knowledge Engineering and Soft Data Paradigms
Data analysis and bioinformatics
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
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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.