prediction
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prediction [2021/01/30 13:38] – [Workflow] krian | prediction [2021/01/30 14:22] (current) – [Model explanation] krian | ||
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You can download the filtered circuits that best differentiate your phenotype. This section is only available when selecting //Rank and filter circuits// option. | You can download the filtered circuits that best differentiate your phenotype. This section is only available when selecting //Rank and filter circuits// option. | ||
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+ | ===== Test report ===== | ||
+ | When you select //Use existing predictor// you will have a different report for your test prediction study. | ||
+ | The test report is divided into four different panels: | ||
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+ | ==== Study Information ==== | ||
+ | As explained before, here you can find the information about the current study. | ||
+ | ==== Input Parameters ==== | ||
+ | The parameters with which the test study was launched, such as the name of the used expression file and the Species. | ||
+ | ==== Circuit values ==== | ||
+ | This matrix file indicates for each “effector circuit” the level of activation calculated using Hipathia method for each sample. | ||
+ | ==== Prediction model ==== | ||
+ | This is the most important result, this table is the predicted design file for your selected expression matrix using a previously trained model. | ||
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===== Workflow ===== | ===== Workflow ===== | ||
The prediction tool is based on a machine learning module, this module of the Hipathia web tool can be summarized as follows: | The prediction tool is based on a machine learning module, this module of the Hipathia web tool can be summarized as follows: |
prediction.1612013880.txt.gz · Last modified: 2021/01/30 13:38 by krian