Sarah-Jane Schramm

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Despite intensive research efforts, within-stage survival rates for melanoma vary widely. Pursuit of molecular biomarkers with improved prognostic significance over clinicohistological measures has produced extensive literature. Reviews have synthesized these data, but none have systematically partitioned high-quality studies from the remainder across(More)
Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An(More)
Large-scale molecular interaction networks are dynamic in nature and are of special interest in the analysis of complex diseases, which are characterized by network-level perturbations rather than changes in individual genes/proteins. The methods developed for the identification of differentially expressed genes or gene sets are not suitable for(More)
Despite intensive research efforts, within-stage survival rates for melanoma vary widely. Pursuit of molecular biomarkers with improved prognostic significance over clinicohistological measures has produced extensive literature. Reviews have synthesized these data, but none have systematically partitioned high-quality studies from the remainder across(More)
Cancer research continues to highlight the extensive genetic diversity that exists both between and within tumors. This intrinsic heterogeneity poses one of the central challenges to predicting patient clinical outcome and the personalization of treatments. Despite progress in some individual tumor types, it is not yet possible to prospectively, accurately(More)
Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. (2015). microRNA and mRNA expression profiling in metastatic melanoma reveal associations with BRAF mutation and patient prognosis. (2014). Network-based biomarkers enhance classical approaches to prognostic gene(More)
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