A robust hidden semi-Markov model with application to aCGH data processing. (1st January 2013)

• Record Type: Journal Article
• Authors: Ding, Jiarui; Shah, Sohrab
• Is Part Of: International journal of data mining and bioinformatics. Volume 8:Number 4(2013)
• Page Start: 427
• Page End: 442
• Publication Date: 2013-01-01
• Subjects: array CGH data -- copy number variation -- hidden semi-Markov models -- discriminative training -- Student's t distribution -- rhsmm; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Hidden semi-Markov models are effective at modelling sequences with succession of homogenous zones by choosing appropriate state duration distributions. To compensate for model mis-specification and provide protection against outliers, we design a robust hidden semi-Markov model with Student's t mixture models as the emission distributions. The proposed approach is used to model array based comparative genomic hybridization data. Experiments conducted on the benchmark data from the Coriell cell lines, and glioblastoma multiforme data illustrate the reliability of the technique.