| Authors: |
Shi, L.; Campbell, G.; Jones, W. D.; Campagne, F.; Wen, Z.; Walker, S. J.; Su, Z.; Chu, T. M.; Goodsaid, F. M.; Pusztai, L.; Shaughnessy, J. D.; Oberthuer, A.; Thomas, R. S.; Paules, R. S.; Fielden, M.; Barlogie, B.; Chen, W.; Du, P.; Fischer, M.; Furlanello, C.; Gallas, B. D.; Ge, X.; Megherbi, D. B.; Symmans, W. F.; Wang, M. D.; Zhang, J.; Bitter, H.; Brors, B.; Bushel, P. R.; Bylesjo, M.; Chen, M.; Cheng, J.; Chou, J.; Davison, T. S.; Delorenzi, M.; Deng, Y.; Devanarayan, V.; Dix, D. J.; Dopazo, J.; Dorff, K. C.; Elloumi, F.; Fan, J.; Fan, S.; Fan, X.; Fang, H.; Gonzaludo, N.; Hess, K. R.; Hong, H.; Huan, J.; Irizarry, R. A.; Judson, R.; Juraeva, D.; Lababidi, S.; Lambert, C. G.; Li, L.; Li, Y.; Li, Z.; Lin, S. M.; Liu, G.; Lobenhofer, E. K.; Luo, J.; Luo, W.; McCall, M. N.; Nikolsky, Y.; Pennello, G. A.; Perkins, R. G.; Philip, R.; Popovici, V.; Price, N. D.; Qian, F.; Scherer, A.; Shi, T.; Shi, W.; Sung, J.; Thierry-Mieg, D.; Thierry-Mieg, J.; Thodima, V.; Trygg, J.; Vishnuvajjala, L.; Wang, S. J.; Wu, J.; Wu, Y.; Xie, Q.; Yousef, W. A.; Zhang, L.; Zhang, X.; Zhong, S.; Zhou, Y.; Zhu, S.; Arasappan, D.; Bao, W.; Lucas, A. B.; Berthold, F.; Brennan, R. J.; Buness, A.; Catalano, J. G.; Chang, C.; Chen, R.; Cheng, Y.; Cui, J., |
| Article Title: |
The Microarray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models |
| Abstract: |
Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis. © 2010 Nature America, Inc. All rights reserved. |
| Keywords: |
survival; controlled study; survival analysis; genetics; mortality; methodology; neoplasm; neoplasms; quality control; animal; animals; liver toxicity; lung toxicity; multiple myeloma; breast cancer; gene expression; gene expression profiling; lung disease; practice guideline; pathology; validation study; breast neoplasms; prediction; disease model; standard; training; evaluation; neuroblastoma; microarray analysis; oligonucleotide array sequence analysis; breast tumor; rat; predictive value of tests; liver disease; liver diseases; rats; dna microarray; disease models, animal; mammals; performance; predictive value; neuroblastomas; guidelines as topic; predictive models; lung diseases; gene expression analysis; predictive control systems; analytical method; clinical reality; gene expression data; independent investigators; microarray data sets; model performance; regulatory agencies; microarrays; microarray quality control ii
|
| Journal Title: |
Nature Biotechnology
|
| Volume: |
28 |
| Issue: |
8 |
| ISSN: |
1087-0156 |
| Publisher: |
Nature Publishing Group
|
| Date Published: |
2010-08-01 |
| Start Page: |
827 |
| End Page: |
838 |
| Language: |
English |
| DOI: |
10.1038/nbt.1665
|
| PUBMED: |
20676074
|
| PROVIDER: |
scopus
|
| PMCID: |
PMC3315840
|
| DOI/URL: |
|
| Notes: |
---
- "Cited By (since 1996): 22"
- "Export Date: 20 April 2011"
- "CODEN: NABIF"
- "Source: Scopus"
|
