The Application of Machine Learning Algorithm In The Analysis of Tissue Microarray; for the Prediction of Clinical Status

  • Cho, Sung-Bum (Seoul National University Biomedical Informatics) ;
  • Kim, Woo-Ho (Department of Pathology, College of Medicine, Seoul National University) ;
  • Kim, Ju-Han (Seoul National University Biomedical Informatics)
  • Published : 2005.09.22


Tissue microarry is one of the high throughput technologies in the post-genomic era. Using tissue microarray, the researchers are able to investigate large amount of gene expressions 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. We identified that Bayesian network classifier algorithm could analyze tissue microarray data and integrating prior knowledge about gastric cancer could 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.