FiGS: a filter-based gene selection workbench for microarray data
作者： Taeho Hwang , Choong-Hyun Sun , Taegyun Yun , Gwan-Su Yi
关键词:
摘要: Background The selection of genes that discriminate disease classes from microarray data is widely used for the identification diagnostic biomarkers. Although various gene methods are currently available and some them have shown excellent performance, no single method can retain best performance all types datasets. It desirable to use a comparative approach find result after rigorous test different methodological strategies given dataset.
biomedcentral.com
springer.com
paperity.org参考文章(18)
Ramón Díaz-Uriarte, Sara Alvarez de Andrés, Gene selection and classification of microarray data using random forest BMC Bioinformatics. ,vol. 7, pp. 3- 3 ,(2006) , 10.1186/1471-2105-7-3
Margaret A. Shipp, Ken N. Ross, Pablo Tamayo, Andrew P. Weng, Jeffery L. Kutok, Ricardo C.T. Aguiar, Michelle Gaasenbeek, Michael Angelo, Michael Reich, Geraldine S. Pinkus, Tane S. Ray, Margaret A. Koval, Kim W. Last, Andrew Norton, T. Andrew Lister, Jill Mesirov, Donna S. Neuberg, Eric S. Lander, Jon C. Aster, Todd R. Golub, Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning Nature Medicine. ,vol. 8, pp. 68- 74 ,(2002) , 10.1038/NM0102-68
U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, A. J. Levine, Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays Proceedings of the National Academy of Sciences of the United States of America. ,vol. 96, pp. 6745- 6750 ,(1999) , 10.1073/PNAS.96.12.6745
Sridhar Ramaswamy, Ken N. Ross, Eric S. Lander, Todd R. Golub, A molecular signature of metastasis in primary solid tumors. Nature Genetics. ,vol. 33, pp. 49- 54 ,(2003) , 10.1038/NG1060
T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, C. D. Bloomfield, E. S. Lander, Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. ,vol. 286, pp. 531- 537 ,(1999) , 10.1126/SCIENCE.286.5439.531
A. Statnikov, C. F. Aliferis, I. Tsamardinos, D. Hardin, S. Levy, A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis Bioinformatics. ,vol. 21, pp. 631- 643 ,(2005) , 10.1093/BIOINFORMATICS/BTI033
Y. Saeys, I. Inza, P. Larranaga, A review of feature selection techniques in bioinformatics Bioinformatics. ,vol. 23, pp. 2507- 2517 ,(2007) , 10.1093/BIOINFORMATICS/BTM344
Bradley Efron, Robert Tibshirani, Improvements on Cross-Validation: The 632+ Bootstrap Method Journal of the American Statistical Association. ,vol. 92, pp. 548- 560 ,(1997) , 10.1080/01621459.1997.10474007
Iñaki Inza, Pedro Larrañaga, Rosa Blanco, Antonio J. Cerrolaza, Filter versus wrapper gene selection approaches in DNA microarray domains Artificial Intelligence in Medicine. ,vol. 31, pp. 91- 103 ,(2004) , 10.1016/J.ARTMED.2004.01.007
Laura J. van 't Veer, Hongyue Dai, Marc J. van de Vijver, Yudong D. He, Augustinus A. M. Hart, Mao Mao, Hans L. Peterse, Karin van der Kooy, Matthew J. Marton, Anke T. Witteveen, George J. Schreiber, Ron M. Kerkhoven, Chris Roberts, Peter S. Linsley, René Bernards, Stephen H. Friend, Gene expression profiling predicts clinical outcome of breast cancer Nature. ,vol. 415, pp. 530- 536 ,(2002) , 10.1038/415530A