Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray.

@article{Kim2004FeatureGO,
  title={Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray.},
  author={Bu-Yeo Kim and Je-Geun Lee and Sunhoo Park and Jae-Yeon Ahn and Yeun-Jin Ju and Jin-Haeng Chung and Chul Ju Han and S Jeong and Young Il Yeom and Sangsoo Kim and Yong-Sung Lee and Chang-min Kim and Eun-Mi Eom and Dong-Hee Lee and Kang-Yell Choi and M. -Il Cho and Kyung-Suk Suh and Dong-Wook Choi and K Lee},
  journal={Biochimica et biophysica acta},
  year={2004},
  volume={1739 1},
  pages={50-61}
}
Recent introduction of a learning algorithm for cDNA microarray analysis has permitted to select feature set to accurately distinguish human cancers according to their pathological judgments. Here, we demonstrate that hepatitis B virus-positive hepatocellular carcinoma (HCC) could successfully be identified from non-tumor liver tissues by supervised learning analysis of gene expression profiling. Through learning and cross-validating HCC sample set, we could identify an optimized set of 44… CONTINUE READING

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