SOMIX: Motifs Discovery in Gene Regulatory Sequences Using Self-Organizing Maps
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...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Book Chapter |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2010
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/11933/ http://ir.unimas.my/id/eprint/11933/1/SOMIX_abstract.pdf |
| Summary: | 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. |
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