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- Publisher Website: 10.1016/j.compbiolchem.2005.02.001
- Scopus: eid_2-s2.0-16344373053
- PMID: 15833436
- WOS: WOS:000228764000002
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Article: Survival analysis of microarray expression data by transformation models
Title | Survival analysis of microarray expression data by transformation models |
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Authors | |
Keywords | Microarray Proportional hazards model Transformation models |
Issue Date | 2005 |
Publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/cbac |
Citation | Computational Biology and Chemistry, 2005, v. 29, p. 91-94 How to Cite? |
Abstract | Many microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Cox's model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816–824] and show that the transformation model provides higher prediction precision than the proportional hazards model. |
Persistent Identifier | http://hdl.handle.net/10722/221691 |
ISSN | 2023 Impact Factor: 2.6 2023 SCImago Journal Rankings: 0.497 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Xu, J | - |
dc.contributor.author | Yang, Y | - |
dc.contributor.author | Ott, J | - |
dc.date.accessioned | 2015-12-04T15:29:08Z | - |
dc.date.available | 2015-12-04T15:29:08Z | - |
dc.date.issued | 2005 | - |
dc.identifier.citation | Computational Biology and Chemistry, 2005, v. 29, p. 91-94 | - |
dc.identifier.issn | 1476-9271 | - |
dc.identifier.uri | http://hdl.handle.net/10722/221691 | - |
dc.description.abstract | Many microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Cox's model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816–824] and show that the transformation model provides higher prediction precision than the proportional hazards model. | - |
dc.language | eng | - |
dc.publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/cbac | - |
dc.relation.ispartof | Computational Biology and Chemistry | - |
dc.subject | Microarray | - |
dc.subject | Proportional hazards model | - |
dc.subject | Transformation models | - |
dc.title | Survival analysis of microarray expression data by transformation models | - |
dc.type | Article | - |
dc.identifier.email | Xu, J: xujf@hku.hk | - |
dc.identifier.authority | Xu, J=rp02086 | - |
dc.identifier.doi | 10.1016/j.compbiolchem.2005.02.001 | - |
dc.identifier.pmid | 15833436 | - |
dc.identifier.scopus | eid_2-s2.0-16344373053 | - |
dc.identifier.volume | 29 | - |
dc.identifier.spage | 91 | - |
dc.identifier.epage | 94 | - |
dc.identifier.isi | WOS:000228764000002 | - |
dc.identifier.issnl | 1476-9271 | - |