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Models Inferring Cellular Networks Using Probabilistic
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متن کاملInferring cellular networks using probabilistic graphical models.
High-throughput genome-wide molecular assays, which probe cellular networks from different perspectives, have become central to molecular biology. Probabilistic graphical models are useful for extracting meaningful biological insights from the resulting data sets. These models provide a concise representation of complex cellular networks by composing simpler submodels. Procedures based on well-...
متن کاملTRANSWESD: inferring cellular networks with transitive reduction
MOTIVATION Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic. RESULTS We p...
متن کاملPerturbation Biology: Inferring Signaling Networks in Cellular Systems
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of r...
متن کامل[Article] Perturbation Biology: Inferring Signaling Networks in Cellular Systems
We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of r...
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ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2007
ISSN: 1471-2105
DOI: 10.1186/1471-2105-8-s6-s5