--- a
+++ b/examples/example_with_precomputed_labels.py
@@ -0,0 +1,88 @@
+from os.path import abspath
+from os.path import split
+from os.path import isdir
+
+from simdeep.simdeep_boosting import SimDeepBoosting
+
+
+def test_instance():
+    """
+    example of SimDeepBoosting starting from precomputed labels
+    To obtain precomputed label files that can be used as an example, please run
+    the `example_with_dummy_data.py` example script
+    """
+
+    PATH_PRECOMPUTED_LABELS = '{0}/../examples/data/TestProject/saved_models_classes'.format(
+        split(abspath(__file__))[0])
+
+    if not isdir(PATH_PRECOMPUTED_LABELS):
+        print('No folder: {0} found' \
+              ' Please run {1}/example_with_dummy_data.py script'.format(
+                  PATH_PRECOMPUTED_LABELS, split(abspath(__file__))[0]))
+        return
+
+    PATH_DATA = '{0}/../examples/data/'.format(split(abspath(__file__))[0])
+
+    #Input file
+    TRAINING_TSV = {'RNA': 'rna_dummy.tsv', 'METH': 'meth_dummy.tsv'}
+    SURVIVAL_TSV = 'survival_dummy.tsv'
+
+    PROJECT_NAME = 'TestProjectPrecomputed'
+    # SEED = 3
+    nb_it = 5 # Number of models to be built
+    nb_threads = 2 # Number of processes to be used to fit individual survival models
+
+    # Import distributed modules
+    import ray
+    ray.init(num_cpus=3)
+
+    boosting = SimDeepBoosting(
+        nb_threads=nb_threads,
+        nb_it=nb_it,
+        split_n_fold=3,
+        survival_tsv=SURVIVAL_TSV,
+        training_tsv=TRAINING_TSV,
+        path_data=PATH_DATA,
+        project_name=PROJECT_NAME,
+        path_results=PATH_DATA,
+        distribute=True, # Option to use ray cluster scheduler (OPTIONAL)
+    )
+
+    boosting.fit_on_pretrained_label_file(
+        labels_files_folder=PATH_PRECOMPUTED_LABELS,
+        file_name_regex="*.tsv")
+    boosting.predict_labels_on_full_dataset()
+
+    boosting.compute_clusters_consistency_for_full_labels()
+    boosting.evalutate_cluster_performance()
+    boosting.collect_cindex_for_test_fold()
+    boosting.collect_cindex_for_full_dataset()
+
+    boosting.compute_feature_scores_per_cluster()
+    boosting.write_feature_score_per_cluster()
+    boosting.collect_number_of_features_per_omic()
+    boosting.compute_pvalue_for_merged_test_fold()
+
+    boosting.load_new_test_dataset(
+        {'RNA': 'rna_dummy.tsv'}, # OMIC file of the test set. It doesnt have to be the same as for training
+        'dummy', # Name of the test test to be used
+        'survival_dummy.tsv', # Survival file of the test set (optional)
+    )
+
+    boosting.predict_labels_on_test_dataset()
+
+    boosting.compute_c_indexes_for_test_dataset()
+    boosting.compute_clusters_consistency_for_test_labels()
+
+    boosting.load_new_test_dataset(
+        {'METH': 'meth_dummy.tsv'}, # OMIC file of the second test set.
+        'dummy_METH', # Name of the second test test
+        'survival_dummy.tsv', # Survival file of the test set (optional)
+    )
+
+    boosting.predict_labels_on_test_dataset()
+
+    ray.shutdown()
+
+if __name__ == '__main__':
+    test_instance()