Analysis of the Tissue Microarray using Bayesian Network Classifier

조성범1), 박유랑2), 이혜원3), 김우호5), 김주한*4)

1)서울대학교 의과대학 생명의료정보학연구실
2)서울대학교 의과대학 생명의료정보학연구실
3)서울대학교 의과대학 생명의료정보학연구실
4)서울대학교 의과대학 생명의료정보학연구실
5)서울대학교 의과대학 병리학 교실

Abstract : The concept of microarray technology, initially developed for the detection of mRNA expression of sample, was extended to embedded, fixed samples. Using tissue microarray, the researchers are able to investigate gene expression profile at the level of DNA, RNA, and protein The important aspect of tissue microarry is its ability to assess a lot of biomarkers which have been used in clinical practice. To manipulate the categorical data of tissue microarray, we applied Bayesian network classifier algorithm. Using Bayesian network classifiers, we integrated prior knowledge about gastric cancer to achieve better performance result. The results showed that relevant integration of prior knowledge promote the prediction accuracy of survival status of the immunohistochemical tissue microarray data of 18 tumor suppressor genes. In conclusion, the application of Bayesian network classifier seemed appropriate for the analysis of the tissue microarray data with clinical information.
keyword : Tissue microarray, Gastric cancer, Bayesian network classifier, Prior knowledge


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