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  • 1
    Language: English
    In: Clinical cancer research : an official journal of the American Association for Cancer Research, 15 October 2008, Vol.14(20), pp.6590-601
    Description: To predict individual survival times for neuroblastoma patients from gene expression data using the cancer survival prediction using automatic relevance determination (CASPAR) algorithm. A first set of oligonucleotide microarray gene expression profiles comprising 256 neuroblastoma patients was generated. Then, CASPAR was combined with a leave-one-out cross-validation to predict individual times for both the whole cohort and subgroups of patients with unfavorable markers, including stage 4 disease (n = 67), unfavorable genetic alterations, intermediate-risk or high-risk stratification by the German neuroblastoma trial, and patients predicted as unfavorable by a recently described gene expression classifier (n = 83). Prediction accuracy of individual survival times was assessed by Kaplan-Meier analyses and time-dependent receiver operator characteristics curve analyses. Subsequently, classification results were validated in an independent cohort (n = 120). CASPAR separated patients with divergent outcome in both the initial and the validation cohort [initial set, 5y-OS 0.94 +/- 0.04 (predicted long survival) versus 0.38 +/- 0.17 (predicted short survival), P 5 years) in subgroups of patients with unfavorable markers with the exception of MYCN-amplified patients (initial set). Confirmatory results with high significance were observed in the validation cohort [stage 4 disease (P = 0.0049), NB2004 intermediate-risk or high-risk stratification (P = 0.0017), and unfavorable gene expression prediction (P = 0.0017)]. CASPAR accurately forecasts individual survival times for neuroblastoma patients from gene expression data.
    Keywords: Gene Expression Profiling ; Biomarkers, Tumor -- Genetics ; Neuroblastoma -- Genetics
    ISSN: 1078-0432
    E-ISSN: 15573265
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  • 2
    Book chapter
    Book chapter
    Berlin, Heidelberg: Springer Berlin Heidelberg
    Language: English
    In: Studies in Computational Intelligence, Computational Intelligence in Bioinformatics, pp.33-74
    Description: Gene regulatory networks describe how cells control the expression of genes, which, together with some additional regulation further downstream, determines the production of proteins essential for cellular function. The level of expression of each gene in the genome is modified by controlling whether and how vigorously it is transcribed to RNA, and subsequently translated to protein. RNA and protein expression will influence expression rates of other genes, thus giving rise to a complicated network structure.An analysis of regulatory processes within the cell will significantly further our understanding of cellular dynamics. It will shed light on normal and abnormal, diseased cellular events, and may provide information on pathways in dire diseases such as cancer. These pathways can provide information on how the disease develops, and what processes are involved in progression. Ultimately, we can hope that this will provide us with new therapeutic approaches and targets for drug design.It is thus no surprise that many efforts have been undertaken to reconstruct gene regulatory networks from gene expression measurements. In this chapter, we will provide an introductory overview over the field. In particular, we will present several different approaches to gene regulatory network inference, discuss their strengths and weaknesses, and provide guidelines on which models are appropriate under what circumstances. In addition, we sketch future developments and open problems.
    Keywords: Engineering ; Appl.Mathematics/Computational Methods of Engineering ; Artificial Intelligence (Incl. Robotics) ; Bioinformatics ; Engineering ; Applied Sciences ; Biology
    ISBN: 9783540768029
    ISBN: 3540768025
    Source: SpringerLink Books
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