SOMIX: Motifs Discovery in Gene Regulatory Sequences Using Self-Organizing Maps

Lee, Nung Kion and Wang, Dianhui (2010) SOMIX: Motifs Discovery in Gene Regulatory Sequences Using Self-Organizing Maps. In: Neural Information Processing. Models and Applications. Lecture Notes in Computer Science, 6444 . Springer Berlin Heidelberg, pp. 242-249. ISBN 978-3-642-17534-3


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We present a clustering algorithm called Self-organizing Map Neural Network with mixed signals discrimination (SOMIX), to discover binding sites in a set of regulatory regions. Our framework integrates a novel intra-node soft competitive procedure in each node model to achieve maximum discrimination of motif from background signals. The intra-node competition is based on an adaptive weighting technique on two different signal models: position specific scoring matrix and markov chain. Simulations on real and artificial datasets showed that, SOMIX could achieve significant performance improvement in terms of sensitivity and specificity over SOMBRERO, which is a well-known SOM based motif discovery tool. SOMIX has also been found promising comparing against other popular motif discovery tools.

Item Type: Book Chapter
Uncontrolled Keywords: self-organizing map, regulatory elements discovery, hybrid model, unimas, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, ipta, education, research, Universiti Malaysia Sarawak
Subjects: Q Science > QA Mathematics
T Technology > T Technology (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Cognitive Sciences and Human Development
Depositing User: Karen Kornalius
Date Deposited: 12 May 2016 04:04
Last Modified: 12 May 2016 04:04

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