Analysis of breast cancer progression using principal component analysis and clustering

Author:

Alexe G.,Dalgin G. S.,Ganesan S.,DeLisi C.,Bhanot G.

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine

Reference24 articles.

1. Alexe G, Dalgin G S, Ramaswamy R, DeLisi C and Bhanot G 2006 Data perturbation independent diagnosis and validation of breast cancer subtypes using clustering and patterns; Cancer Informatics 2 243–274

2. Benjamini Y and Hochberg Y 1995 Controlling the false discovery rate: a practical and powerful approach to multiple testing; J. R. Stat. Soc. Series B 57 289–300

3. Bussey K J, Kane D, Sunshine M, Narasimhan S, Nishizuka S, Reinhold W C, Zeeberg B, Ajay W and Weinstein J N 2004 MatchMiner: a tool for batch navigation among gene and gene product identifiers; Genome Biol. 4 R27

4. Cheng C-H, Fu A W and Zhang Y 1999 Entropy-based subspace clustering for mining numerical data; in Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining (San Diego, California, United States ACM Press)

5. Dempster A, Laird N and Rubin D 1977 Maximum likelihood from incomplete data via the EM algorithm; J. R. Stat. Soc. Series B 39 1–38

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