scholarly journals Bayesian Estimation of Multivariate Autoregressive Hidden Markov Model with Application to Breast Cancer Biomarker Modeling

2017 ◽  
Author(s):  
Hamid El Maroufy ◽  
El Houcine Hibbah ◽  
Abdelmajid Zyad ◽  
Taib Ziad
Keyword(s):  
Breast Cancer ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Bayesian Estimation ◽  
Hidden Markov ◽  
Cancer Biomarker ◽  
Multivariate Autoregressive

scholarly journals Using hidden Markov model to predict recurrence of breast cancer based on sequential patterns in gene expression profiles

2020 ◽  
Vol 111 ◽  
pp. 103570
Author(s):  
Mohammadreza Momenzadeh ◽  
Mohammadreza Sehhati ◽  
Hossein Rabbani
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Sequential Patterns

scholarly journals Fusion of Hidden Markov Model and Wavelet Transform to Recognise Breast Cancer

2015 ◽  
Vol 37 ◽  
pp. 139
Author(s):  
Sayedeh Somayeh Hosaini ◽  
Mehran Emadi
Keyword(s):  
Breast Cancer ◽  
Wavelet Transform ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Specific Capacity ◽  
New Method ◽  
Image Feature ◽  
Transform Method ◽  
Joint Distributions

Public health is affected by the health and vitality of women who are the pillars of the families. Among the women between 15 to 54 years old, breast cancer is one of the most important causes of mortality. Every 13 minutes, a woman loses her life due to breast cancer. Statistics show that %12.6 of women suffer from breast cancer. Mammography can be considered as one of the most efficient methods of breast cancer diagnosis; however, this method has still its own limitations. The quality of mammography images can be enhanced by using image processing and the disease can be diagnosed more easily by using image feature extraction. Using Hidden Markov Model and wavelet transform method, a new method for the detection of suspicious areas of breast cancer tumors will be proposed in this study. In addition to the detection of tumors, this method can determine the mass percentage to estimate the progression of the disease. In this study, Markov Model with a tree structure was used to extract statistical properties of the wavelet components. The specific capacity of Markov Model to extract information about edges and the regions of protrusions in image tissues increases the accuracy of cancerous areas detection. The present study sought to estimate the appropriate label (clustering) of the pixels of an image under investigation to segment cancerous areas. Therefore, certain joint distributions were assumed for the pixels of a region or a class and then the maximum similarities between different areas of the examined image and joint distributions were examined through the Maximum-Likelihood Estimation method. To evaluate the proposed method and analyze the results, a combination of the MIAS and PADN databases comprising 150 images was used. The results indicated that the proposed method is more accurate than methods which are solely based on wavelet transform. In the new method, the obtained detection rate was %96 while in the wavelet transform method, it has been reported %71.5 indicating the precision of the proposed method.

Application of a Hidden Markov Model in Identifying Target Genes in Breast Cancer

2020 ◽  
pp. 523-532
Author(s):  
Parisa Torkaman
Keyword(s):  
Breast Cancer ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Genes ◽  
Hidden Markov ◽  
Transition Probability ◽  
Number Of Patients ◽  
Gene Regulatory

Breast cancer is one of the most common malignant cancers among women with increasing number of patients. Gene regulatory network and identifying target genes for cancer treatment, and reducing breast cancer death rates is of great importance medically. This study aims to model gene regulatory network of breast cancer using hidden Markov model which greatly aids doctors in early diagnosis and faster treatment of breast cancer using identification of target genes. In this study, gene expressions of $206$ patients diagnosed with four subtypes of breast cancer including, Basal, Her2, LumA, LumB, were obtained from the Cancer Genome Atlas (TCGA). $8$ genes with the verified interaction among them were investigated by hidden Markov model of gene regulatory network and target genes. with the results of transition probability matrix, FADD, TNFRSF10B, CASP8 are the target genes in the mentioned cancer subtypes so that genes that their transmit probabilities are more than an initial value of $0.125$ are regulatory genes and transmit matrix identifies the probability of the mentioned cancers regarding gene expression level.

Multiple Sequence Alignment and Profile Analysis of Protein Family Utsing Hidden Markov Model

2012 ◽  
Vol 2 (6) ◽  
pp. 208-211
Author(s):  
Navjot Kaur ◽  
◽  
Rajbir Singh Cheema ◽  
Harmandeep Singh Harmandeep Singh
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Profile Analysis ◽  
Hidden Markov ◽  
Protein Family ◽  
Multiple Sequence

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

A Fast Voice Command Recognition Algorithm Based on the Hidden Markov Model Stationary Distribution

Vestnik MEI ◽  
2018 ◽  
Vol 5 (5) ◽  
pp. 65-72
Author(s):  
Pavel A. Paramonov ◽  
◽  
Ivan V. Ognev ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Stationary Distribution ◽  
Hidden Markov ◽  
Recognition Algorithm ◽  
Voice Command

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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