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Using Hidden Markov Model to Predict Recurrence of Breast Cancer Based on Sequential Patterns in Gene Expression Profiles Publisher Pubmed



Momenzadeh M1 ; Sehhati M1, 2, 3 ; Rabbani H1, 2
Authors
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Authors Affiliations
  1. 1. Department of Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Department of Bioinformatics, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

Source: Journal of Biomedical Informatics Published:2020


Abstract

A new approach is presented to predict breast cancer recurrence through gene expression profiles using hidden Markov models (HMM). In this regard, 322 genes were selected from 44 published gene lists related to breast cancer prognosis. Afterwards, using gene set enrichment analysis, 922 gene sets were found from subsets of genes with the same biological meaning. In order to extract the sequential patterns from gene expression data, we ranked the gene sets using appropriate criteria and used HMM in which the ranked gene sets considered as observation sequences and hidden states represented priority of gene sets for discriminating between expression profiles. In this experiment, seven publicly available microarray datasets, including 1271 breast tumor samples, were used to classify cancer patients into two groups according to risk of recurrence. Our experiments indicated the greater performance and more robustness of the proposed model compared with other widely used classification methods. © 2020
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