Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data
Open Access
- 28 October 2003
- journal article
- review article
- Published by Springer Nature in British Journal of Cancer
- Vol. 89 (9), 1599-1604
- https://doi.org/10.1038/sj.bjc.6601326
Abstract
DNA microarrays are a potentially powerful technology for improving diagnostic classification, treatment selection and therapeutics development. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. This paper provides a review of the key features to be observed in developing diagnostic and prognostic classification systems based on gene expression profiling and some of the pitfalls to be aware of in reading reports of microarray-based studies.Keywords
This publication has 24 references indexed in Scilit:
- The Use of Molecular Profiling to Predict Survival after Chemotherapy for Diffuse Large-B-Cell LymphomaNew England Journal of Medicine, 2002
- A Paradigm for Class Prediction Using Gene Expression ProfilesJournal of Computational Biology, 2002
- Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression DataJournal of the American Statistical Association, 2002
- Gene expression profiling predicts clinical outcome of breast cancerNature, 2002
- Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learningNature Medicine, 2002
- Strong Feature Sets from Small SamplesJournal of Computational Biology, 2002
- Multiclass cancer diagnosis using tumor gene expression signaturesProceedings of the National Academy of Sciences, 2001
- Tissue Classification with Gene Expression ProfilesJournal of Computational Biology, 2000
- Distinct types of diffuse large B-cell lymphoma identified by gene expression profilingNature, 2000
- A neural network model for survival dataStatistics in Medicine, 1995