diff --git a/server.R b/server.R
index cc7cdd7..785efd4 100644
--- a/server.R
+++ b/server.R
@@ -6,20 +6,37 @@ source("init.R")
 source("scatter_plot.R")
 
 server <- function(input, output) {
-    #' This expression applies all user defined filters to the available
-    #' results.
+    #' This reactive expression applies all user defined filters as well as the
+    #' desired ranking weights to the results.
     results <- reactive({
+        # Select the species preset.
+
         results <- if (input$species == "all") {
             results_all
         } else {
             results_replicative
         }
 
-        results[
+        # Apply user defined filters.
+
+        results <- results[
             cluster_length >= input$length &
                 cluster_mean >= input$range[1] * 1000000 &
                 cluster_mean <= input$range[2] * 1000000
         ]
+
+        # Compute scoring factors and the weighted score.
+
+        cluster_max <- results[, max(cluster_length)]
+        results[, cluster_score := cluster_length / cluster_max]
+        
+        results[, score := input$clustering / 100 * cluster_score +
+            input$correlation / 100 * r_mean]
+
+        # Order the results based on their score. The resulting index will be
+        # used as the "rank".
+
+        setorder(results, -score)
     })
 
     output$genes <- renderDT({
diff --git a/ui.R b/ui.R
index 685f907..58b3afd 100644
--- a/ui.R
+++ b/ui.R
@@ -27,8 +27,30 @@ ui <- fluidPage(
                 "length",
                 "Minimum cluster size",
                 min = 0,
-                max = 30,
-                value = 10
+                max = 50,
+                value = c(0, 15),
+                step = 0.1
+            )
+        ),
+        wellPanel(
+            h3("Ranking"),
+            sliderInput(
+                "clustering",
+                "Size of largest cluster",
+                post = "%",
+                min = 0,
+                max = 100,
+                step = 1,
+                value = 100
+            ),
+            sliderInput(
+                "correlation",
+                "Correlation with known genes",
+                post = "%",
+                min = 0,
+                max = 100,
+                step = 1,
+                value = 100
             )
         ),
         wellPanel(