--- a
+++ b/examples/create_autoencoder_from_scratch.py
@@ -0,0 +1,63 @@
+"""
+Create a new autoencoder model from scratch using user defined .tsv input files
+"""
+
+from simdeep.extract_data import LoadData
+from simdeep.simdeep_analysis import SimDeep
+
+from simdeep.config import PATH_DATA
+from simdeep.config import PATH_TO_SAVE_MODEL
+
+
+def main():
+    """ """
+
+    # Defining the path for the data
+    # (we will the default path defined in config.py) but can be overloaded
+    path_data = PATH_DATA
+    print('path to access the .tsv files: '+ path_data)
+
+    # Defining the path to save the autoencoder
+    path_to_save_model = PATH_TO_SAVE_MODEL
+    print('path where the models will be saved:' + path_to_save_model)
+
+    # the dataset to be used
+    # Here we will combine only two omics to create the autoencoder:
+    # RNA and MIR.
+    # We will use the dummy dataset available in the example folder
+    #These files should be inside the dataset_path folder
+    tsv_files = {
+        'RNA': 'rna_dummy.tsv',
+        'MIR': 'mir_dummy.tsv'
+    }
+
+    # survival file to be used
+    survival_file = 'survival_dummy.tsv'
+
+    # Metadata file (optional)
+    metadata_file = "metadata_dummy.tsv"
+
+    # class to load and prepare the data
+    dataset = LoadData(path_data=path_data,
+                       training_tsv=tsv_files,
+                       survival_tsv=survival_file,
+                       metadata_tsv=metadata_file # Optional
+                       )
+
+    simDeep = SimDeep(dataset=dataset,
+                      path_to_save_model=path_to_save_model,
+                      seed=2020
+    )
+    # dataset must be loaded
+    simDeep.load_training_dataset()
+    # model construction
+    simDeep.fit()
+
+    # predict on full dataset
+    simDeep.predict_labels_on_full_dataset()
+    # Finally, saving the model
+    simDeep.save_encoders('encoder_example.h5')
+
+
+if __name__ == "__main__":
+    main()