Matches in SemOpenAlex for { <https://semopenalex.org/work/W2564779849> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W2564779849 abstract "B55 Objectives: Molecular high throughput data offers unprecedented opportunities for discovery including new diagnostics and personalized treatments in a wide range of cancers. Given the novel nature of such data, standard analysis principles either do not apply or are not sufficient. On the other hand, recent high-throughput analysis guidelines are not standardized, they have not been independently validated, and there is no consensus regarding best practices. The present study illustrates these problems in two recent cancer studies by identifying subtle yet critical errors that jeopardize the studies9 findings and conclusions. Methods to avoid such errors are proposed. Methods: We examine the methodological validity and identify subtle errors in the highly-cited studies of [1] and [2] We test the impact of the above errors on study conclusions by re-analyzing the original and simulated data using protocols where the identified errors are systematically removed. Results: Changes in the error metric, the methods employed for its estimation and statistical testing, and the classifier, allow previously undetected predictive signals to be identified in 6 out of 7 datasets of [1]. This refutes the original study conclusions that microarray data may not predict cancer outcomes and that studies require thousands of patients for the purpose of outcome prediction. Changes in the statistical tests for SNP selection and signature error estimation reveal that all SNPs identified by [2] are not, in fact, statistically significant at the chosen level and that the original study classifier does not perform better than chance. Conclusions: Critical to fulfilling the promise of omics cancer research is the sound analysis of high-throughput data. The field is in dire need for validated protocols and standardized best practices in order to protect researchers from critical errors and to allow them to use their data effectively and their resources efficiently. References: 1. Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005;365(9458):488-92. 2. Hu N, Wang C, Hu Y, Yang HH, Giffen C, Tang ZZ, et al. Genome-wide association study in esophageal cancer using GeneChip mapping 10K array. Cancer Res 2005 Apr 1;65(7):2542-6." @default.
- W2564779849 created "2017-01-06" @default.
- W2564779849 creator A5027728817 @default.
- W2564779849 creator A5039062539 @default.
- W2564779849 creator A5042352003 @default.
- W2564779849 creator A5058793854 @default.
- W2564779849 creator A5061333322 @default.
- W2564779849 creator A5070582381 @default.
- W2564779849 creator A5075226492 @default.
- W2564779849 date "2007-10-01" @default.
- W2564779849 modified "2023-09-27" @default.
- W2564779849 title "Subtle but critical errors in deriving cancer signatures and markers from high-throughput molecular data: Two high-profile case studies" @default.
- W2564779849 hasPublicationYear "2007" @default.
- W2564779849 type Work @default.
- W2564779849 sameAs 2564779849 @default.
- W2564779849 citedByCount "0" @default.
- W2564779849 crossrefType "journal-article" @default.
- W2564779849 hasAuthorship W2564779849A5027728817 @default.
- W2564779849 hasAuthorship W2564779849A5039062539 @default.
- W2564779849 hasAuthorship W2564779849A5042352003 @default.
- W2564779849 hasAuthorship W2564779849A5058793854 @default.
- W2564779849 hasAuthorship W2564779849A5061333322 @default.
- W2564779849 hasAuthorship W2564779849A5070582381 @default.
- W2564779849 hasAuthorship W2564779849A5075226492 @default.
- W2564779849 hasConcept C105795698 @default.
- W2564779849 hasConcept C124101348 @default.
- W2564779849 hasConcept C154945302 @default.
- W2564779849 hasConcept C183905921 @default.
- W2564779849 hasConcept C33923547 @default.
- W2564779849 hasConcept C41008148 @default.
- W2564779849 hasConcept C70721500 @default.
- W2564779849 hasConcept C86803240 @default.
- W2564779849 hasConcept C95623464 @default.
- W2564779849 hasConceptScore W2564779849C105795698 @default.
- W2564779849 hasConceptScore W2564779849C124101348 @default.
- W2564779849 hasConceptScore W2564779849C154945302 @default.
- W2564779849 hasConceptScore W2564779849C183905921 @default.
- W2564779849 hasConceptScore W2564779849C33923547 @default.
- W2564779849 hasConceptScore W2564779849C41008148 @default.
- W2564779849 hasConceptScore W2564779849C70721500 @default.
- W2564779849 hasConceptScore W2564779849C86803240 @default.
- W2564779849 hasConceptScore W2564779849C95623464 @default.
- W2564779849 hasLocation W25647798491 @default.
- W2564779849 hasOpenAccess W2564779849 @default.
- W2564779849 hasPrimaryLocation W25647798491 @default.
- W2564779849 hasRelatedWork W1995029188 @default.
- W2564779849 hasRelatedWork W2007563785 @default.
- W2564779849 hasRelatedWork W2031897491 @default.
- W2564779849 hasRelatedWork W2134978098 @default.
- W2564779849 hasRelatedWork W2191425288 @default.
- W2564779849 hasRelatedWork W2279541396 @default.
- W2564779849 hasRelatedWork W2331595304 @default.
- W2564779849 hasRelatedWork W2748521476 @default.
- W2564779849 hasRelatedWork W2766936598 @default.
- W2564779849 hasRelatedWork W2783256549 @default.
- W2564779849 hasRelatedWork W2810436388 @default.
- W2564779849 hasRelatedWork W2902761813 @default.
- W2564779849 hasRelatedWork W2943001064 @default.
- W2564779849 hasRelatedWork W2972391117 @default.
- W2564779849 hasRelatedWork W3011148401 @default.
- W2564779849 hasRelatedWork W3042215127 @default.
- W2564779849 hasRelatedWork W3099004071 @default.
- W2564779849 hasRelatedWork W600482503 @default.
- W2564779849 hasRelatedWork W629739262 @default.
- W2564779849 hasRelatedWork W84093231 @default.
- W2564779849 hasVolume "13" @default.
- W2564779849 isParatext "false" @default.
- W2564779849 isRetracted "false" @default.
- W2564779849 magId "2564779849" @default.
- W2564779849 workType "article" @default.