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Denoising Algorithm based on Relevance network Topology (DART) is an unsupervised algorithm that estimates an activity score for a pathway in a gene expression matrix, following a denoising step.〔 In DART, a weighted average is used where the weights reflect the degree of the nodes in the pruned network. The denoising step removes prior information that is inconsistent with a data set. This strategy substantially improves unsupervised predictions of pathway activity that are based on a prior model, which was learned from a different biological system or context.〔 Pre-existing methods such as gene set enrichment analysis method attempt to infer. However, it did not construct a structured list of genes. SPIA (Signaling Pathway Impact analysis) is a method that uses the phenotype information to evaluate the pathway activity between two phenotypes. However, it does not identify the pathway gene subset that could be used to differentiate individual samples.〔 CORG is used to identify a relevant gene subset. It is a supervised method, which does not perform as well as DART in analyzing independent data set〔 Understanding molecular pathway activity is crucial for risk assessment, clinical diagnosis and treatment. Meta-analysis of complex genomic data is often associated with difficulties such as extracting useful information from big data, eliminating confounding factors and providing more sensible interpretation. Different approaches have been taken to highlight the identification of relevant pathway in order to provide better gene expression prediction. == Method == File:DART method.jpg|Caption1 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Denoising Algorithm based on Relevance network Topology」の詳細全文を読む スポンサード リンク
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