The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data
Open accessAbstract
An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer.
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The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Self-citationCluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Gene expression profiling predicts clinical outcome of breast cancer
van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536
Khan, J., Wei, J. S., Ringnér, M., Saal, L. H., Ladanyi, M., Westermann, F., Berthold, F., Schwab, M., Antonescu, C. R., Peterson, C., et al. (2001) · Nature Medicine · doi:10.1038/89044 · Cited by 2,644
Gene-expression profiles predict survival of patients with lung adenocarcinoma
Beer, D. G., Kardia, S. L. R., Huang, C.-C., Giordano, T. J., Levin, A. M., Misek, D. E., Lin, L., Chen, G., Gharib, T. G., Thomas, D. G., et al. (2002) · Nature Medicine · doi:10.1038/nm733 · Cited by 2,025
Cited by
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Schiffman, E., Ohrbach, R., Truelove, E., Look, J., Anderson, G., Goulet, J.-P., List, T., Svensson, P., Gonzalez, Y., Lobbezoo, F., et al. (2014) · Journal of Oral & Facial Pain and Headache · doi:10.11607/jop.1151 · Cited by 4,567
“135 Although the new DC/TMD protocol will be an important tool for future research projects addressing underlying TMD mechanisms and etiologies, it has limitations.”
BackgroundKatzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0482-1 · Cited by 1,995
“It is a common practice in medical applications to determine the relationship between a patient’s observable covariates and his or her risk of an event [13, 3, 26].”
BackgroundDeletion of IKZF1 and Prognosis in Acute Lymphoblastic Leukemia
Mullighan, C. G., Su, X., Zhang, J., Radtke, I., Phillips, L. A. A., Miller, C. B., Ma, J., Liu, W., Cheng, C., Schulman, B. A., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808253 · Cited by 1,422
Systemic Immunity Is Required for Effective Cancer Immunotherapy
Spitzer, M. H., Carmi, Y., Reticker-Flynn, N. E., Kwek, S. S., Madhireddy, D., Martins, M. M., Gherardini, P. F., Prestwood, T. R., Chabon, J., Bendall, S. C., et al. (2017) · Cell · doi:10.1016/j.cell.2016.12.022 · Cited by 963
Metadata
- Authors
- Bair, E., & Tibshirani, R.
- Venue
- PLoS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2004-04-13
- Volume
- 2
- Issue
- 4
- Pages
- e108
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- OpenAlex
- W2018116321
- Cited by
- 754
- References
- 5
- FWCI
- 7.00
- Type
- journal article
- Language
- en
- Open access
- gold
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