Literature DB >> 12648678

Profiling cancer.

Marco Ciro1, Adrian P Bracken, Kristian Helin.   

Abstract

In the past couple of years, several very exciting studies have demonstrated the enormous power of gene-expression profiling for cancer classification and prediction of patient survival. In addition to promising a more accurate classification of cancer and therefore better treatment of patients, gene-expression profiling can result in the identification of novel potential targets for cancer therapy and a better understanding of the molecular mechanisms leading to cancer.

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Year:  2003        PMID: 12648678     DOI: 10.1016/s0955-0674(03)00007-3

Source DB:  PubMed          Journal:  Curr Opin Cell Biol        ISSN: 0955-0674            Impact factor:   8.382


  3 in total

1.  Microarray analysis distinguishes differential gene expression patterns from large and small colony Thymidine kinase mutants of L5178Y mouse lymphoma cells.

Authors:  Tao Han; Jianyong Wang; Weida Tong; Martha M Moore; James C Fuscoe; Tao Chen
Journal:  BMC Bioinformatics       Date:  2006-09-06       Impact factor: 3.169

2.  Improved variance estimation of classification performance via reduction of bias caused by small sample size.

Authors:  Ulrika Wickenberg-Bolin; Hanna Göransson; Mårten Fryknäs; Mats G Gustafsson; Anders Isaksson
Journal:  BMC Bioinformatics       Date:  2006-03-13       Impact factor: 3.169

3.  Gene expression profiling of epithelial ovarian tumours correlated with malignant potential.

Authors:  Susanne Warrenfeltz; Stephen Pavlik; Susmita Datta; Eileen T Kraemer; Benedict Benigno; John F McDonald
Journal:  Mol Cancer       Date:  2004-10-07       Impact factor: 27.401

  3 in total

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