BIOMAT Database Logo BIOMAT Database Logo

A robust hidden semi-Markov model with application to aCGH data processing. 2013

Jiarui Ding, and Sohrab Shah
Department of Computer Science, University of British Columbia, Vancouver, Canada. jiaruid@cs.ubc.ca

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.

UI MeSH Term Description Entries
D008390 Markov Chains A stochastic process such that the conditional probability distribution for a state at any future instant, given the present state, is unaffected by any additional knowledge of the past history of the system. Markov Process,Markov Chain,Chain, Markov,Chains, Markov,Markov Processes,Process, Markov,Processes, Markov
D002877 Chromosomes, Human Very long DNA molecules and associated proteins, HISTONES, and non-histone chromosomal proteins (CHROMOSOMAL PROTEINS, NON-HISTONE). Normally 46 chromosomes, including two sex chromosomes are found in the nucleus of human cells. They carry the hereditary information of the individual. Chromosome, Human,Human Chromosome,Human Chromosomes
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D001330 Electronic Data Processing Applications that store and process large quantities of data. Automatic Data Processing,Bar Codes,Computer Data Processing,Data Processing, Automatic,Information Processing, Automatic,Optical Readers,Information Processing,Automatic Information Processing,Bar Code,Codes, Bar,Data Processing, Computer,Data Processing, Electronic,Optical Reader,Processing, Automatic Data,Processing, Automatic Information,Processing, Computer Data,Processing, Electronic Data,Processing, Information
D045744 Cell Line, Tumor A cell line derived from cultured tumor cells. Tumor Cell Line,Cell Lines, Tumor,Line, Tumor Cell,Lines, Tumor Cell,Tumor Cell Lines
D055028 Comparative Genomic Hybridization A method for comparing two sets of chromosomal DNA by analyzing differences in the copy number and location of specific sequences. It is used to look for large sequence changes such as deletions, duplications, amplifications, or translocations. Array Comparative Genomic Hybridization,Array-Based Comparative Genomic Hybridization,Comparative Genome Hybridization,Array Based Comparative Genomic Hybridization,Comparative Genome Hybridizations,Comparative Genomic Hybridizations,Genome Hybridization, Comparative,Genome Hybridizations, Comparative,Genomic Hybridization, Comparative,Genomic Hybridizations, Comparative,Hybridization, Comparative Genome,Hybridization, Comparative Genomic,Hybridizations, Comparative Genome,Hybridizations, Comparative Genomic
D023281 Genomics The systematic study of the complete DNA sequences (GENOME) of organisms. Included is construction of complete genetic, physical, and transcript maps, and the analysis of this structural genomic information on a global scale such as in GENOME WIDE ASSOCIATION STUDIES. Functional Genomics,Structural Genomics,Comparative Genomics,Genomics, Comparative,Genomics, Functional,Genomics, Structural

Related Publications

Jiarui Ding, and Sohrab Shah
The discovery of processing stages: analyzing EEG data with hidden semi-Markov models.
March 2015, NeuroImage,
Jiarui Ding, and Sohrab Shah
Multiple indicator hidden Markov model with an application to medical utilization data.
January 2009, Statistics in medicine,
Jiarui Ding, and Sohrab Shah
biomvRhsmm: genomic segmentation with hidden semi-Markov model.
January 2014, BioMed research international,
Jiarui Ding, and Sohrab Shah
Robust sequential data modeling using an outlier tolerant hidden Markov model.
September 2009, IEEE transactions on pattern analysis and machine intelligence,
Jiarui Ding, and Sohrab Shah
A mixed non-homogeneous hidden Markov model for categorical data, with application to alcohol consumption.
April 2012, Statistics in medicine,
Jiarui Ding, and Sohrab Shah
A hidden semi-Markov model for estimating burst suppression EEG.
July 2019, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference,
Jiarui Ding, and Sohrab Shah
A semi-supervised Hidden Markov model-based activity monitoring system.
January 2011, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference,
Jiarui Ding, and Sohrab Shah
A Latent Hidden Markov Model for Process Data.
March 2024, Psychometrika,
Jiarui Ding, and Sohrab Shah
Hidden Markov model variants and their application.
September 2006, BMC bioinformatics,
Jiarui Ding, and Sohrab Shah
Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data.
July 2009, Bioinformatics (Oxford, England),
Copied contents to your clipboard!