Differences

This shows you the differences between two versions of the page.

--- prediction [2015/12/22 14:38] – aamadoz
+++ prediction [2017/05/24 14:33] (current) – external edit 127.0.0.1
@@ Line 1: / Line 1: @@
 ====== Prediction ======
-The aim of the //Prediction// tool is to take advantage of the signalling circuit activities to distinguish between phenotypes. Depending on the study design, we can perform a two group comparison or a correlation with a continuous variable.
+===== Train =====
-HiPathia Prediction uses the signalling value of mechanism-based biomarkers to compute a SVM prediction model with cross-validation. Moreover, previously obtained models could be used to predict the phenotype of new samples.
+The aim of the //Prediction// tool is to take advantage of the module activities to distinguish between phenotypes.
+Metabolizer Prediction uses the module activity values to compute a RF or SVM prediction model with cross-validation. Moreover, previously obtained models can be used to predict the phenotype of new samples.
+RF: Random Forest, SVM: Support Vector Machines
 The tool can be accessed from the main menu bar, by clicking on the //Prediction// button, see [[workflow|Workflow]] for further information.
@@ Line 13: / Line 17: @@
   * [[Input data|Input data]]
   * [[Design data|Design data]]
+  * [[Train Method | Train Method]]
   * [[Species|Species]]
-  * [[Parameters|Parameters]]
-  * [[Pathways|Pathways]]
   * [[Job information|Job information]]
@@ Line 22: / Line 25: @@
 === OUTPUT ===
-The results page of the Prediction tool includes different output results.
+The results page of the Prediction tool includes different output results. You can download any table or image showed in the results page by clicking on the name right before it. You can also download calculated module activity values and CV statistics of trained model by clicking on //**Module activity values**// and //**Statistics**// respectively. CV results also represented by area under the Receiver Operating characteristic (ROC) curve.
+CV: Cross Validation
+The results are divided in different panels:
+  * [[Input Parameters Prediction|Input Parameters]]
+  * [[path values prediction | Path values]]
+  * [[Prediction Model|Prediction Model]]
+===== Test =====
+  * [[Input Parameters Prediction Test|Input Parameters]]
+  * [[path values prediction test | Path values]]
+  * [[Prediction Results |Prediction Results]]