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Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures
Kostka, Dennis und Spang, Rainer (2008) Microarray Based Diagnosis Profits from Better Documentation of Gene Expression Signatures. PLoS Computational Biology 4 (2), e22.Veröffentlichungsdatum dieses Volltextes: 17 Aug 2016 12:12
Artikel
DOI zum Zitieren dieses Dokuments: 10.5283/epub.34377
Zusammenfassung
Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray ...
Microarray gene expression signatures hold great promise to improve diagnosis and prognosis of disease. However, current documentation standards of such signatures do not allow for an unambiguous application to study-external patients. This hinders independent evaluation, effectively delaying the use of signatures in clinical practice. Data from eight publicly available clinical microarray studies were analyzed and the consistency of study-internal with study-external diagnoses was evaluated. Study-external classifications were based on documented information only. Documenting a signature is conceptually different from reporting a list of genes. We show that even the exact quantitative specification of a classification rule alone does not define a signature unambiguously. We found that discrepancy between study-internal and study-external diagnoses can be as frequent as 30% (worst case) and 18% (median). By using the proposed documentation by value strategy, which documents quantitative preprocessing information, the median discrepancy was reduced to 1%. The process of evaluating microarray gene expression diagnostic signatures and bringing them to clinical practice can be substantially improved and made more reliable by better documentation of the signatures.
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| Dokumentenart | Artikel | ||||||
| Titel eines Journals oder einer Zeitschrift | PLoS Computational Biology | ||||||
| Verlag: | PUBLIC LIBRARY SCIENCE | ||||||
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| Ort der Veröffentlichung: | SAN FRANCISCO | ||||||
| Band: | 4 | ||||||
| Nummer des Zeitschriftenheftes oder des Kapitels: | 2 | ||||||
| Seitenbereich: | e22 | ||||||
| Datum | Februar 2008 | ||||||
| Institutionen | Medizin > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) Informatik und Data Science > Fachbereich Bioinformatik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang) | ||||||
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| Stichwörter / Keywords | ACUTE LYMPHOBLASTIC-LEUKEMIA; DNA MICROARRAYS; CANCER OUTCOMES; BREAST-CANCER; PREDICTION; CLASSIFICATION; ADENOCARCINOMA; NORMALIZATION; VALIDATION; LYMPHOMA; | ||||||
| Dewey-Dezimal-Klassifikation | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin | ||||||
| Status | Veröffentlicht | ||||||
| Begutachtet | Ja, diese Version wurde begutachtet | ||||||
| An der Universität Regensburg entstanden | Zum Teil | ||||||
| URN der UB Regensburg | urn:nbn:de:bvb:355-epub-343776 | ||||||
| Dokumenten-ID | 34377 |
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