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    Keywords: CANCER ; CANCER CELLS ; CELLS ; CELL ; COMBINATION ; Germany ; human ; MODEL ; MODELS ; PATHWAY ; PATHWAYS ; ALGORITHMS ; GENE ; GENES ; microarray ; RNA ; COMPLEX ; COMPLEXES ; DNA ; BIOLOGY ; BREAST ; breast cancer ; BREAST-CANCER ; microarrays ; SIGNALING PATHWAY ; SIGNALING PATHWAYS ; CANCER-CELLS ; signaling ; review ; INTERFERENCE ; SIGNALING NETWORK ; signaling networks ; Nested effects models ; Perturbation data ; Signaling pathway inference
    Abstract: Targeted gene perturbations have become a major tool to gain insight into complex cellular processes. In combination with the measurement of downstream effects via DNA microarrays, this approach can be used to gain insight into signaling pathways. Nested Effects Models were first introduced by Markowetz et al. as a probabilistic method to reverse engineer signaling cascades based on the nested structure of downstream perturbation effects. The basic framework was substantially extended later on by Frohlich et al., Markowetz et al., and Tresch and Markowetz. In this paper, we present a review of the complete methodology with a detailed comparison of so far proposed algorithms on a qualitative and quantitative level. As an application, we present results on estimating the signaling network between 13 genes in the ER-a pathway of human MCF-7 breast cancer cells. Comparison with the literature shows a substantial overlap
    Type of Publication: Journal article published
    PubMed ID: 19358219
    Signatur Availability
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