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
  • [21] A hidden absorbing semi-Markov model for informatively censored temporal data: Learning and inference
    Alaa, Ahmed M.
    van der Schaar, Mihaela
    Journal of Machine Learning Research, 2018, 19 : 1 - 62
  • [22] Mesoscale spatial variation of rainfall through a hidden semi-Markov model of breakpoint data
    Sansom, J
    Thompson, CS
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2003, 108 (D8)
  • [23] A hidden semi-Markov model with missing data and multiple observation sequences for mobility tracking
    Yu, SZ
    Kobayashi, H
    SIGNAL PROCESSING, 2003, 83 (02) : 235 - 250
  • [24] State duration and interval modeling in hidden semi-Markov model for sequential data analysis
    Hiromi Narimatsu
    Hiroyuki Kasai
    Annals of Mathematics and Artificial Intelligence, 2017, 81 : 377 - 403
  • [25] APPLICATION ISSSUES OF THE SEMI-MARKOV RELIABILITY MODEL
    Rudnicki, Jacek
    POLISH MARITIME RESEARCH, 2015, 22 (01) : 55 - 64
  • [26] Using Hidden Semi-Markov Model for Learning Behavior in Smarthomes
    Paris, Arnaud
    Arbaoui, Selma
    Cislo, Nathalie
    El-Amraoui, Adnen
    Ramdani, Nacim
    2015 INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2015, : 752 - 757
  • [27] Reconstructing Individual Activity Trajectories by Hidden Semi-Markov Model
    Han, Zixuan
    Wan, Zijian
    Guo, Wanyi
    Ren, Chang
    2018 26TH INTERNATIONAL CONFERENCE ON GEOINFORMATICS (GEOINFORMATICS 2018), 2018,
  • [28] A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
    Alaa, Ahmed M.
    van der Schaar, Mihaela
    JOURNAL OF MACHINE LEARNING RESEARCH, 2018, 19 : 1 - 62
  • [29] Machine condition recognition via hidden semi-Markov model
    Yang, Wenhui
    Chen, Lu
    COMPUTERS & INDUSTRIAL ENGINEERING, 2021, 158 (158)
  • [30] Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models
    Adams, Stephen
    Beling, Peter A.
    Cogill, Randy
    IEEE ACCESS, 2016, 4 : 1642 - 1657
12345