期刊文献+

Kernel Dimensionality Reduction Evaluation on Various Dimensions of Effective Subspaces for Cancer Patient Survival Analysis

Kernel Dimensionality Reduction Evaluation on Various Dimensions of Effective Subspaces for Cancer Patient Survival Analysis
下载PDF
导出
出处 《通讯和计算机(中英文版)》 2011年第8期619-623,共5页 Journal of Communication and Computer
关键词 生存分析 子空间 癌症病人 内核 尺寸 DNA微阵列 基因分类 评价 Semi supervised learning, kernel dimensionality reduction, cancer, survival analysis.
  • 相关文献

参考文献9

  • 1L.J. van't Veer et al, Gene expression profiling predicts clinical outcome of breast cancer, Nature, 2002, pp. 530-536.
  • 2I. Wasito, S.Z. Hashim, S. Sukmaningrum, Iterative local Gaussian clustering for expressed genes identification linked to malignancy of human colorectal carcinoma, Bioinformation (2007) 175-181.
  • 3C.Y. Soon, I. Wasito, S.Z. Mohd Hashim, Kernel dimensionality reduction evaluation on various dimensions of effective subspaces for cancer survival analysis, Proceeding in International Conference ISSPA 2010, Kuala Lumpur, Malaysia, 2010.
  • 4K. Fukumizu, R. Bach, M.I. Jordan, Kernel Dimensionality Reduction for Supervised Learning, Advances in NIPS, Vol. 16, 2004.
  • 5E. Bair, R. Tibshirani, Semi-supervised methods to predict patient survival from gene expression data, PLoS Biology 2 (2004) 511-522.
  • 6N Aronszajn, Theory of reproducing kernels, Trans. Amer. Math. Soc. 69 (1950) 337-404.
  • 7D.R. Cox, D. Oakes,Analysis of Survival Data, Chapman and Hall, London, 1984, p. 208.
  • 8T.S. Furey, et al., Support vector machine classification and validation cancer tissues samples using microarray expression data, Technical University of California, Santa Cruz, 2000.
  • 9Rosenwald et al., The use of molecular profiling to predict survival after chemotherapy for diffuse large-b-cell lymphoma, N Engl J Med 346 (25) 2002.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部