Literature DB >> 14728164

Bioinformatics for medical diagnostics: assessment of microarray data in the context of clinical databases.

M Dugas1, S Merk, S Breit, C Schoch, T Haferlach, S Kääb.   

Abstract

MOTIVATION: To identify genes suitable for medical diagnostics microarray data is assessed in the context of clinical databases, which store complex information about the patient phenotype. The wealth of data and lacking standards make it difficult to analyse this kind of data.
RESULTS: We present a workflow for exploratory analysis of microarray data together with clinical data consisting of four steps: definition of clinically meaningful research questions in a masterfile, generation of analysis files, selection and characterization of differentially expressed genes, and estimation of classification accuracy. We applied this workflow to large data sets from the field of cardiology and oncology (n~500 patients). Systematic data management of microarray data and clinical data helps to make results more transparent and comparable.

Entities:  

Mesh:

Year:  2003        PMID: 14728164      PMCID: PMC1480146     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  7 in total

1.  Support vector machine classification and validation of cancer tissue samples using microarray expression data.

Authors:  T S Furey; N Cristianini; N Duffy; D W Bednarski; M Schummer; D Haussler
Journal:  Bioinformatics       Date:  2000-10       Impact factor: 6.937

2.  A comprehensive leukemia database: integration of cytogenetics, molecular genetics and microarray data with clinical information, cytomorphology and immunophenotyping.

Authors:  M Dugas; C Schoch; S Schnittger; T Haferlach; S Danhauser-Riedl; W Hiddemann; D Messerer; K Uberla
Journal:  Leukemia       Date:  2001-12       Impact factor: 11.528

3.  Complexity of biomedical data models in cardiology: the Intranet-based AF registry.

Authors:  M Dugas; E Hoffmann; S Janko; S Hahnewald; T Matis; J Miller; Ch v Bary; A Farnbacher; V Vogler; K Uberla
Journal:  Comput Methods Programs Biomed       Date:  2002-04       Impact factor: 5.428

4.  Molecular classification of multiple tumor types.

Authors:  C H Yeang; S Ramaswamy; P Tamayo; S Mukherjee; R M Rifkin; M Angelo; M Reich; E Lander; J Mesirov; T Golub
Journal:  Bioinformatics       Date:  2001       Impact factor: 6.937

5.  Acute myeloid leukemias with reciprocal rearrangements can be distinguished by specific gene expression profiles.

Authors:  Claudia Schoch; Alexander Kohlmann; Susanne Schnittger; Benedikt Brors; Martin Dugas; Susanne Mergenthaler; Wolfgang Kern; Wolfgang Hiddemann; Roland Eils; Torsten Haferlach
Journal:  Proc Natl Acad Sci U S A       Date:  2002-07-08       Impact factor: 11.205

6.  A gene-expression signature as a predictor of survival in breast cancer.

Authors:  Marc J van de Vijver; Yudong D He; Laura J van't Veer; Hongyue Dai; Augustinus A M Hart; Dorien W Voskuil; George J Schreiber; Johannes L Peterse; Chris Roberts; Matthew J Marton; Mark Parrish; Douwe Atsma; Anke Witteveen; Annuska Glas; Leonie Delahaye; Tony van der Velde; Harry Bartelink; Sjoerd Rodenhuis; Emiel T Rutgers; Stephen H Friend; René Bernards
Journal:  N Engl J Med       Date:  2002-12-19       Impact factor: 91.245

7.  Molecular characterization of acute leukemias by use of microarray technology.

Authors:  Alexander Kohlmann; Claudia Schoch; Susanne Schnittger; Martin Dugas; Wolfgang Hiddemann; Wolfgang Kern; Torsten Haferlach
Journal:  Genes Chromosomes Cancer       Date:  2003-08       Impact factor: 5.006

  7 in total

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