diff --git a/R/adjacency.R b/R/adjacency.R
new file mode 100644
index 0000000..cd0d958
--- /dev/null
+++ b/R/adjacency.R
@@ -0,0 +1,81 @@
+# Score genes based on their proximity to the reference genes.
+#
+# This method finds the distance value with the maximum density for each gene
+# (i.e. the mode of its estimated distribution). Genes are scored by comparing
+# those distance values with the values of the reference genes.
+adjacency <- function(preset, progress = NULL) {
+    species_ids <- preset$species_ids
+    gene_ids <- preset$gene_ids
+    reference_gene_ids <- preset$reference_gene_ids
+
+    cached("adjacency", c(species_ids, gene_ids, reference_gene_ids), {
+        # Get the virtual distance value with the highest density.
+        compute_densest_distance <- function(distances) {
+            if (length(distances <= 2)) {
+                mean(distances)
+            } else {
+                d <- stats::density(distances)
+                d$x[which.max(d$y)]
+            }
+        }
+
+        # Filter distances by species and gene and find the distance with the
+        # highest density of values for each gene.
+        data <- geposan::distances[
+            species %chin% species_ids & gene %chin% gene_ids,
+            .(densest_distance = compute_densest_distance(distance)),
+            by = gene
+        ]
+
+        # Compute the absolute value of the difference between the provided
+        # densest distance value in comparison to the mean of the densest
+        # distances of the comparison genes.
+        compute_difference <- function(densest_distance, comparison_ids) {
+            # Get the mean of the densest distances of the reference genes.
+            mean_densest_distance <- data[
+                gene %chin% comparison_ids,
+                mean(densest_distance)
+            ]
+
+            abs(densest_distance - mean_densest_distance)
+        }
+
+        # Compute the differences to the reference genes.
+        data[
+            !gene %chin% reference_gene_ids,
+            difference := compute_difference(
+                densest_distance,
+                reference_gene_ids
+            )
+        ]
+
+        if (!is.null(progress)) {
+            progress(0.5)
+        }
+
+        # Exclude the reference gene itself when computing its difference.
+        data[
+            gene %chin% reference_gene_ids,
+            difference := compute_difference(
+                densest_distance,
+                reference_gene_ids[reference_gene_ids != gene]
+            ),
+            by = gene
+        ]
+
+        # Compute the final score by normalizing the difference.
+        data[, score := 1 - difference / max(difference)]
+
+        if (!is.null(progress)) {
+            progress(1.0)
+        }
+
+        structure(
+            list(
+                results = data[, .(gene, score)],
+                details = data
+            ),
+            class = "geposan_method_results"
+        )
+    })
+}
diff --git a/R/analyze.R b/R/analyze.R
index 4fbd2a6..3acaf9b 100644
--- a/R/analyze.R
+++ b/R/analyze.R
@@ -34,6 +34,7 @@ analyze <- function(preset, progress = NULL) {
         "clusteriness" = clusteriness,
         "correlation" = correlation,
         "neural" = neural,
+        "adjacency" = adjacency,
         "proximity" = proximity
     )
 
diff --git a/R/preset.R b/R/preset.R
index e3b0edd..46ae739 100644
--- a/R/preset.R
+++ b/R/preset.R
@@ -11,14 +11,10 @@
 #'
 #'  - `clusteriness` How much the gene distances to the nearest telomere
 #'    cluster across species.
-#'  - `clusteriness_positions` The same as `clusteriness` but using absolute
-#'    gene positions instead of distances.
 #'  - `correlation` The mean correlation of gene distances to the nearest
 #'    telomere across species.
-#'  - `correlation_positions` Correlation using position data.
-#'  - `neural` Assessment by neural network trained using distances.
-#'  - `neural_positions` Assessment by neural network trained using absolute
-#'    position data.
+#'  - `neural` Assessment by neural network trained on the reference genes.
+#'  - `adjacency` Proximity to reference genes.
 #'  - `proximity` Mean proximity to telomeres.
 #'
 #' Available optimization targets are:
@@ -42,6 +38,7 @@ preset <- function(methods = c(
                        "clusteriness",
                        "correlation",
                        "neural",
+                       "adjacency",
                        "proximity"
                    ),
                    species_ids = NULL,