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Kostka, Dennis ; Spang, Rainer

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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Details

DokumentenartArtikel
Titel eines Journals oder einer ZeitschriftPLoS Computational Biology
Verlag:PUBLIC LIBRARY SCIENCE
Ort der Veröffentlichung:SAN FRANCISCO
Band:4
Nummer des Zeitschriftenheftes oder des Kapitels:2
Seitenbereich:e22
DatumFebruar 2008
InstitutionenMedizin > Institut für Funktionelle Genomik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Informatik und Data Science > Fachbereich Bioinformatik > Lehrstuhl für Statistische Bioinformatik (Prof. Spang)
Identifikationsnummer
WertTyp
18282081PubMed-ID
10.1371/journal.pcbi.0040022DOI
Stichwörter / KeywordsACUTE LYMPHOBLASTIC-LEUKEMIA; DNA MICROARRAYS; CANCER OUTCOMES; BREAST-CANCER; PREDICTION; CLASSIFICATION; ADENOCARCINOMA; NORMALIZATION; VALIDATION; LYMPHOMA;
Dewey-Dezimal-Klassifikation600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin
StatusVeröffentlicht
BegutachtetJa, diese Version wurde begutachtet
An der Universität Regensburg entstandenZum Teil
URN der UB Regensburgurn:nbn:de:bvb:355-epub-343776
Dokumenten-ID34377

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