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

被引：4
|
作者：
Ding, Jiarui [1 ,2 ]
Shah, Sohrab [1 ,3 ]
机构：
[1] Univ British Columbia, Dept Comp Sci, Vancouver, BC V5T 4E6, Canada
[2] BC Canc Agcy, Dept Mol Biol, Vancouver, BC V5T 4E6, Canada
[3] Univ British Columbia, Dept Pathol, Vancouver, BC V5T 4E6, Canada
来源：
INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS | 2013年 / 8卷 / 04期
关键词：
array CGH data; copy number variation; hidden semi-Markov models; discriminative training; Student's t distribution; rhsmm; ARRAY; SEGMENTATION;
D O I：
10.1504/IJDMB.2013.056616
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
引用
收藏
页码：427 / 442
页数：16
相关论文
共 50 条
  • [1] Robust hidden semi-Markov modeling of array CGH data
    Ding, Jiarui
    Shah, Sohrab P.
    2010 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE, 2010, : 603 - 608
  • [2] A Hierarchical Hidden Semi-Markov Model for Modeling Mobility Data
    Baratchi, Mitra
    Meratnia, Nirvana
    Havinga, Paul J. M.
    Skidmore, Andrew K.
    Toxopeus, Bert A. K. G.
    UBICOMP'14: PROCEEDINGS OF THE 2014 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, 2014, : 401 - 412
  • [3] Application of Hidden Semi-Markov Model to 3′ splice sites identification
    Feng, XC
    Qian, MP
    Deng, MH
    Ma, XT
    Yan, XT
    PROGRESS IN BIOCHEMISTRY AND BIOPHYSICS, 2004, 31 (05) : 455 - 458
  • [4] Adaptive training for hidden semi-Markov model
    Yamagishi, J
    Kobayashi, T
    2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING, 2005, : 365 - 368
  • [5] Hidden semi-Markov model for anomaly detection
    Tan, Xiaobin
    Xi, Hongsheng
    APPLIED MATHEMATICS AND COMPUTATION, 2008, 205 (02) : 562 - 567
  • [6] The discovery of processing stages: Analyzing EEG data with hidden semi-Markov models
    Borst, Jelmer P.
    Anderson, John R.
    NEUROIMAGE, 2015, 108 : 60 - 73
  • [7] Hidden semi-Markov models
    Yu, Shun-Zheng
    ARTIFICIAL INTELLIGENCE, 2010, 174 (02) : 215 - 243
  • [8] Nonhomogeneous hidden semi-Markov models for toroidal data
    Lagona, Francesco
    Mingione, Marco
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2024,
  • [9] SEMI-MARKOV MODEL AND ITS APPLICATION
    JAIN, RK
    BIOMETRICAL JOURNAL, 1987, 29 (04) : 439 - 443
  • [10] Application of continuous hidden semi-Markov model in bearing performance degradation assessment
    Li, Wei-Hua
    Li, Jing
    Zhang, Shao-Hui
    Li, Wei-Hua, 1600, Nanjing University of Aeronautics an Astronautics (27): : 613 - 620
12345