Quick Start Guide

This notebook demonstrates the core functionality of the BioNeuralNet package. It covers data loading, network generation, network embedding via GNNs, subject representation integration, downstream disease prediction, evaluation metrics, clustering, and use of external tools.”

BioNeuralNet offers a number of classes and functions.

from bioneuralnet.downstream_task import SubjectRepresentation
from bioneuralnet.downstream_task import DPMON
from bioneuralnet.clustering import CorrelatedPageRank
from bioneuralnet.clustering import CorrelatedLouvain
from bioneuralnet.clustering import HybridLouvain

from bioneuralnet.metrics import omics_correlation
from bioneuralnet.metrics import cluster_correlation
from bioneuralnet.metrics import louvain_to_adjacency
from bioneuralnet.metrics import evaluate_rf
from bioneuralnet.metrics import plot_performance_three
from bioneuralnet.metrics import plot_variance_distribution
from bioneuralnet.metrics import plot_variance_by_feature
from bioneuralnet.metrics import plot_performance
from bioneuralnet.metrics import plot_embeddings
from bioneuralnet.metrics import plot_network
from bioneuralnet.metrics import compare_clusters

from bioneuralnet.utils import get_logger
from bioneuralnet.utils import rdata_to_df
from bioneuralnet.utils import variance_summary
from bioneuralnet.utils import zero_fraction_summary
from bioneuralnet.utils import expression_summary
from bioneuralnet.utils import correlation_summary
from bioneuralnet.utils import explore_data_stats
from bioneuralnet.utils import preprocess_clinical
from bioneuralnet.utils import clean_inf_nan
from bioneuralnet.utils import select_top_k_variance
from bioneuralnet.utils import select_top_k_correlation
from bioneuralnet.utils import select_top_randomforest
from bioneuralnet.utils import top_anova_f_features
from bioneuralnet.utils import prune_network
from bioneuralnet.utils import prune_network_by_quantile
from bioneuralnet.utils import network_remove_low_variance
from bioneuralnet.utils import network_remove_high_zero_fraction
from bioneuralnet.utils import gen_similarity_graph
from bioneuralnet.utils import gen_correlation_graph
from bioneuralnet.utils import gen_threshold_graph
from bioneuralnet.utils import gen_gaussian_knn_graph
from bioneuralnet.utils import gen_lasso_graph
from bioneuralnet.utils import gen_mst_graph
from bioneuralnet.utils import gen_snn_graph

from bioneuralnet.datasets import DatasetLoader
from bioneuralnet.external_tools import SmCCNet

Load Data

import pandas as pd
from bioneuralnet.datasets import DatasetLoader

loader = DatasetLoader("example1")
omics1, omics2, phenotype, clinical = loader.load_data()

display(omics1)
display(omics2)
display(phenotype)
display(clinical)

	Gene_1	Gene_2	Gene_3	Gene_4	Gene_5	Gene_6	Gene_7	Gene_8	Gene_9	Gene_10	...	Gene_491	Gene_492	Gene_493	Gene_494	Gene_495	Gene_496	Gene_497	Gene_498	Gene_499	Gene_500
Samp_1	22.485701	40.353720	31.025745	20.847206	26.697293	30.205449	23.512005	33.677622	19.430333	30.260153	...	12.967210	13.334132	13.262070	13.608147	13.338606	15.247613	13.603157	14.727795	13.400950	12.769172
Samp_2	37.058850	34.052233	33.487020	23.531461	26.754628	31.735945	22.795952	29.301536	14.936397	30.823015	...	11.886247	13.241386	12.445773	13.400288	12.587993	15.048676	12.018609	14.513958	12.066379	12.583460
Samp_3	20.530767	31.669623	35.189567	20.952544	25.018826	32.157235	25.069464	22.853719	18.220225	23.092805	...	12.198357	12.869640	12.841290	13.127437	12.607377	14.398177	12.554904	14.339382	12.891962	12.760553
Samp_4	33.186888	38.480880	18.897097	31.823300	34.049383	38.799887	24.106468	12.397175	13.724255	27.703085	...	12.197502	13.312536	11.645099	13.933684	12.103087	15.133432	12.436027	15.116888	12.810732	12.972879
Samp_5	28.961981	41.060494	28.494956	18.374495	30.815238	24.004535	29.616296	24.364045	11.409338	33.599828	...	14.157141	13.386978	13.007109	13.826532	12.343930	15.280175	13.363962	14.008675	12.479124	12.156407
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Samp_354	24.520652	28.595409	31.299666	32.095379	33.659730	29.353948	29.871659	25.089732	14.437588	27.261916	...	11.748228	13.096453	12.768936	13.228560	11.595884	15.251601	12.055375	15.530606	13.644383	13.018032
Samp_355	31.252789	28.988087	29.574195	31.189288	32.098841	26.849138	22.619669	28.006670	8.615549	30.838843	...	11.460876	12.994504	13.325608	13.386970	12.335143	14.966097	13.998975	13.250821	12.947672	13.161434
Samp_356	24.894826	25.944887	30.852641	26.705158	30.102546	37.197952	35.032309	34.775576	13.354611	29.697348	...	12.884821	12.785372	12.954534	13.906209	12.074805	15.519179	12.742078	14.445011	12.129990	13.844271
Samp_357	17.034337	38.574705	25.095201	37.062442	35.417758	29.753145	25.855997	25.603043	13.180794	31.188598	...	13.278454	13.445404	12.457436	14.036252	13.412114	14.158416	13.284701	13.662274	12.943670	13.996352
Samp_358	20.839167	27.099788	31.038453	19.410859	31.818995	33.493625	22.769369	22.869481	16.839248	24.644476	...	12.519057	13.308512	12.261076	11.211687	12.058898	15.349873	13.166209	15.295902	13.257230	13.178058

358 rows × 500 columns

	Mir_1	Mir_2	Mir_3	Mir_4	Mir_5	Mir_6	Mir_7	Mir_8	Mir_9	Mir_10	...	Mir_91	Mir_92	Mir_93	Mir_94	Mir_95	Mir_96	Mir_97	Mir_98	Mir_99	Mir_100
Samp_1	15.223913	17.545826	15.784719	14.891983	10.348205	9.689755	11.093630	13.189246	12.526561	10.311609	...	7.551462	5.407878	13.933045	10.287521	10.213316	10.609973	14.554996	10.955650	11.422531	10.862970
Samp_2	16.306965	16.672830	13.361529	14.488549	12.660905	11.333613	11.254930	13.457435	13.279747	10.428848	...	6.862413	7.309226	13.586180	11.975544	11.496937	10.653742	14.414225	11.811004	12.413667	10.719110
Samp_3	16.545119	16.735005	14.617472	17.845267	13.822790	11.329333	11.171749	12.715107	13.787771	10.364577	...	6.874958	7.754733	13.847029	12.424403	10.930177	10.255484	13.570352	11.311925	11.072915	11.418794
Samp_4	13.986899	16.207432	16.293078	17.725286	12.300565	9.844108	11.586551	11.878702	14.594392	10.936651	...	9.615623	6.693593	13.840728	12.245466	10.836894	11.502232	15.483399	10.812811	10.121957	11.039089
Samp_5	16.338332	17.393869	16.397925	15.853725	13.387675	10.599414	12.355466	12.450426	13.778779	10.405327	...	9.146865	8.104206	13.903452	11.423350	9.997107	10.744586	14.465583	11.730166	12.206151	10.724849
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
Samp_354	15.065065	16.079830	14.635616	17.013845	11.612843	9.850100	11.375670	12.870558	13.873811	11.369083	...	8.110691	8.124900	14.316491	12.599296	10.302683	11.286541	14.478041	11.141163	11.102427	11.993050
Samp_355	15.997576	15.448951	15.355566	16.501752	11.701778	11.323521	11.610163	12.733208	13.650662	10.770903	...	8.711232	6.977555	14.805837	12.283549	10.847157	9.833187	14.037476	11.082880	11.708466	10.654141
Samp_356	15.206862	14.395378	16.218001	16.044955	13.650741	11.057462	13.324846	13.070428	16.109746	11.253359	...	7.497926	6.840175	15.048896	12.615673	10.583973	12.708186	16.325254	10.789635	10.830833	10.983455
Samp_357	14.474129	15.482863	15.512549	15.136613	14.531277	10.713539	11.632242	13.792981	13.486404	10.870028	...	7.902283	7.339226	14.295151	11.853855	10.439473	11.897782	16.684790	10.847185	11.491449	11.684467
Samp_358	15.094188	16.047304	15.298871	17.022665	15.046043	10.931386	12.289466	14.327210	13.533348	9.792684	...	8.017328	7.814296	14.267007	12.117150	11.832061	11.021241	14.533734	12.015799	11.551237	11.221372

358 rows × 100 columns

	phenotype
Samp_1	235.067423
Samp_2	253.544991
Samp_3	234.204994
Samp_4	281.035429
Samp_5	245.447781
...	...
Samp_354	236.120451
Samp_355	222.572359
Samp_356	268.472285
Samp_357	235.808167
Samp_358	213.886123

358 rows × 1 columns

	Age	Gender	BMI	Chronic_Bronchitis	Emphysema	Asthma
PatientID
Samp_1	78	0	31.2	1	1	0
Samp_2	68	1	19.2	1	0	0
Samp_3	54	1	19.3	0	1	1
Samp_4	47	1	36.2	0	0	1
Samp_5	60	1	26.2	0	1	1
...	...	...	...	...	...	...
Samp_354	71	0	23.0	1	0	1
Samp_355	62	1	25.5	0	1	1
Samp_356	61	0	21.1	1	0	0
Samp_357	64	0	37.6	0	0	1
Samp_358	61	1	31.3	1	0	0

358 rows × 6 columns

Generate Global Network using SmCCNet

from bioneuralnet.external_tools import SmCCNet

smccnet = SmCCNet(
    phenotype_df=phenotype,
    omics_dfs=[omics1, omics2],
    data_types=["genes", "proteins"],
)
global_network, smccnet_clusters = smccnet.run()

print("Global network shape:", global_network.shape)
print("Number of SmCCnet clusters:", len(smccnet_clusters))
display(global_network.head())

display(global_network.head())

	Gene_1	Gene_2	Gene_3	Gene_4	Gene_5	Gene_6	Gene_7	Gene_8	Gene_9	Gene_10	...	Mir_91	Mir_92	Mir_93	Mir_94	Mir_95	Mir_96	Mir_97	Mir_98	Mir_99	Mir_100
Gene_1	0.000000	0.160757	0	0	0.043592	0.271547	0.277287	0	0.009604	0.043405	...	0	0	0	0	0	0.004766	0	0	0	0
Gene_2	0.160757	0.000000	0	0	0.038431	0.243888	0.239016	0	0.008589	0.037873	...	0	0	0	0	0	0.005787	0	0	0	0
Gene_3	0.000000	0.000000	0	0	0.000000	0.000000	0.000000	0	0.000000	0.000000	...	0	0	0	0	0	0.000000	0	0	0	0
Gene_4	0.000000	0.000000	0	0	0.000000	0.000000	0.000000	0	0.000000	0.000000	...	0	0	0	0	0	0.000000	0	0	0	0
Gene_5	0.043592	0.038431	0	0	0.000000	0.066452	0.060837	0	0.002207	0.010718	...	0	0	0	0	0	0.000953	0	0	0	0

5 rows × 600 columns

Generate Network Embeddings using GNNEmbedding

from bioneuralnet.network_embedding import GNNEmbedding

merged_omics = pd.concat([omics1, omics2], axis=1)

gnn = GNNEmbedding(
    adjacency_matrix=global_network,
    omics_data=merged_omics,
    phenotype_data=phenotype,
    clinical_data=clinical,
    phenotype_col="phenotype",
    model_type="GAT",
    hidden_dim=64,
    layer_num=4,
    dropout=True,
    num_epochs=100,
    lr=1e-3,
    weight_decay=1e-4,
    seed=119,
)
gnn.fit()
embeddings = gnn.embed(as_df=True)

display(embeddings.head())

	Embed_1	Embed_2	Embed_3	Embed_4	Embed_5	Embed_6	Embed_7	Embed_8	Embed_9	Embed_10	...	Embed_55	Embed_56	Embed_57	Embed_58	Embed_59	Embed_60	Embed_61	Embed_62	Embed_63	Embed_64
Gene_1	0.037078	0.010051	-0.036622	0.022973	0.029106	-0.024114	0.031300	0.028295	0.047263	0.024042	...	-0.017544	0.042265	0.017295	-0.030349	0.029958	-0.020222	0.029427	-0.026153	-0.025061	0.028961
Gene_2	0.037078	0.010051	-0.036622	0.022973	0.029106	-0.024114	0.031300	0.028295	0.047263	0.024042	...	-0.017544	0.042265	0.017295	-0.030349	0.029958	-0.020222	0.029427	-0.026153	-0.025061	0.028961
Gene_3	0.038274	0.007089	-0.050431	0.026016	0.025564	-0.028543	0.030443	0.017762	0.045573	0.018522	...	-0.017076	0.048518	0.014335	-0.023130	0.029575	-0.023226	0.028018	-0.041073	-0.041075	0.030907
Gene_4	0.032268	0.003910	-0.033076	0.024749	0.044768	-0.014179	0.017665	0.014499	0.052139	0.028020	...	-0.022137	0.030999	0.015979	-0.034100	0.028120	-0.025319	0.027917	-0.012606	-0.028836	0.026390
Gene_5	0.037078	0.010051	-0.036622	0.022973	0.029106	-0.024114	0.031300	0.028295	0.047263	0.024042	...	-0.017544	0.042265	0.017295	-0.030349	0.029958	-0.020222	0.029427	-0.026153	-0.025061	0.028961

5 rows × 64 columns

Embeddings visualization

from bioneuralnet.metrics import plot_embeddings

# Using our embeddings instance we get the necessary labels for the graph.
node_labels = gnn._prepare_node_labels()
embeddings_array = embeddings.values

embeddings_plot = plot_embeddings(embeddings_array, node_labels)

2025-03-07 12:31:43,802 - bioneuralnet.network_embedding.gnn_embedding - INFO - Preparing node labels by correlating each omics feature with phenotype column 'phenotype'.
2025-03-07 12:31:43,803 - bioneuralnet.network_embedding.gnn_embedding - INFO - Index(['Samp_1', 'Samp_2', 'Samp_3', 'Samp_4', 'Samp_5', 'Samp_6', 'Samp_7',
       'Samp_8', 'Samp_9', 'Samp_10',
       ...
       'Samp_349', 'Samp_350', 'Samp_351', 'Samp_352', 'Samp_353', 'Samp_354',
       'Samp_355', 'Samp_356', 'Samp_357', 'Samp_358'],
      dtype='object', length=358)
2025-03-07 12:31:43,875 - bioneuralnet.network_embedding.gnn_embedding - INFO - Node labels prepared successfully.

Integrate Embeddings into Omics Data with SubjectRepresentation

• Let use these omics to enrich the representation of the original dataset.

• The SubjectRepresentation function takes our previously generated embeddings and our original Omics Dataset and associated Phenotype

• This function will use the embeddings to enrich the orignal dataset. For more details and how this is performed please view our GNN Embeddings for Multi-Omics tab.

from bioneuralnet.downstream_task import SubjectRepresentation

graph_embed = SubjectRepresentation(
    omics_data=merged_omics,
    embeddings=embeddings,
    phenotype_data=phenotype,
    phenotype_col="phenotype",
    reduce_method="PCA",
    tune=False,
)
enhanced_omics = graph_embed.run()

display(enhanced_omics.head())

	Gene_213	Gene_177	Mir_78	Gene_50	Gene_335	Gene_491	Gene_187	Mir_74	Gene_76	Gene_357	...	Gene_371	Mir_84	Mir_40	Gene_266	Gene_62	Gene_239	Gene_496	Mir_33	Gene_495	Gene_249
Samp_1	31.972913	7.749811	4.713763	-0.749105	-0.823750	-0.784272	-0.611546	-6.206424	-0.764613	0.006680	...	7.257692	9.188055	-0.359187	14.058017	-0.777809	-3.507224	0.545008	-8.300834	3.433189	-17.325618
Samp_2	30.562847	7.616120	3.262150	-0.715228	-0.904390	-0.718894	-0.778848	-7.043132	-0.751795	0.006527	...	6.266241	10.170368	-0.334464	14.220815	-0.754667	-3.325996	0.537897	-7.037332	3.239990	-16.226066
Samp_3	30.319072	7.959335	5.177822	-0.799192	-0.890603	-0.737770	-0.916597	-6.473910	-0.757998	0.006260	...	6.520219	9.402382	-0.359254	14.802170	-0.734408	-3.654560	0.514646	-7.261344	3.244979	-17.947806
Samp_4	33.532754	8.136440	4.885794	-0.715119	-0.800914	-0.737719	-0.601052	-7.031316	-0.744343	0.005747	...	6.069317	8.792790	-0.320809	14.675955	-0.778705	-3.532869	0.540927	-6.572756	3.115182	-19.260010
Samp_5	32.241288	8.248584	3.934224	-0.797891	-0.813945	-0.856240	-0.324133	-6.991057	-0.719048	0.007457	...	6.832945	9.096281	-0.414235	14.231641	-0.729173	-3.555468	0.546172	-7.145498	3.177172	-20.107202

5 rows × 600 columns

Comparing results for prediction task

from bioneuralnet.metrics import evaluate_rf
from bioneuralnet.metrics import plot_performance

y_phenotype = phenotype.values
X_enriched_omics = enhanced_omics.values
X_raw_omics = merged_omics.values

accuracy_with_embeddings = evaluate_rf(X_enriched_omics, y_phenotype, mode="regression")
accuracy_alone = evaluate_rf(X_enriched_omics, y_phenotype, mode="regression")

c:\Users\ramos\Desktop\GitHub\BioNeuralNet\.venv\lib\site-packages\sklearn\base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
c:\Users\ramos\Desktop\GitHub\BioNeuralNet\.venv\lib\site-packages\sklearn\base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)

plot_performance(accuracy_with_embeddings, accuracy_alone, "Raw Omics vs Enriched Omics")

Disease Prediction using DPMON(Disease Prediction using Multi-Omics Networks)

• DPMON is a classification task.

• We will adjust our phenotype from continus value to a class from 0 to 3

from bioneuralnet.datasets import DatasetLoader
import numpy as np

loader = DatasetLoader("example1")
omics1, omics2, phenotype, clinical = loader.load_data()
display(phenotype)

min_val = phenotype["phenotype"].min()
max_val = phenotype["phenotype"].max()

# linspace creates an array of evenly spaced values
bins = np.linspace(min_val, max_val, 5)

phenotype["phenotype"] = pd.cut(phenotype["phenotype"], bins=bins, labels=[0, 1, 2, 3], include_lowest=True)
count_values = phenotype["phenotype"].value_counts(sort=False)

	phenotype
Samp_1	235.067423
Samp_2	253.544991
Samp_3	234.204994
Samp_4	281.035429
Samp_5	245.447781
...	...
Samp_354	236.120451
Samp_355	222.572359
Samp_356	268.472285
Samp_357	235.808167
Samp_358	213.886123

358 rows × 1 columns

Phenotype after:

display(phenotype)
display(count_values)

	phenotype
Samp_1	1
Samp_2	2
Samp_3	1
Samp_4	3
Samp_5	2
...	...
Samp_354	1
Samp_355	1
Samp_356	2
Samp_357	1
Samp_358	1

358 rows × 1 columns

phenotype
0     38
1    158
2    141
3     21
Name: count, dtype: int64

Visualizing the variance distribution and feature variance

from bioneuralnet.metrics import plot_variance_distribution

fig = plot_variance_distribution(omics2, bins=100)

2025-03-07 12:31:51,166 - bioneuralnet.metrics.plot - INFO - Computed variances for each feature.
2025-03-07 12:31:51,209 - bioneuralnet.metrics.plot - INFO - Variance distribution plot generated.

from bioneuralnet.metrics import plot_variance_by_feature

fig2 = plot_variance_by_feature(omics2.iloc[:, 0:20])

2025-03-07 12:31:51,338 - bioneuralnet.metrics.plot - INFO - Computed variances for each feature for index plot.
2025-03-07 12:31:51,348 - bioneuralnet.metrics.plot - INFO - Variance vs. feature index plot generated.

from bioneuralnet.downstream_task import DPMON

dpmon = DPMON(
    adjacency_matrix=global_network,
    omics_list=[omics1, omics2],
    phenotype_data=phenotype,
    clinical_data=clinical,
    model="GAT",
    layer_num=4,
    num_epochs=100,
    lr=1e-3,
    weight_decay=1e-4,
    tune=False,
    gpu=False,
    output_dir="dpmon_output"
)
predictions = dpmon.run()

DPMON Predictions

display(predictions[0])

	Actual	Predicted
0	1	1
1	2	2
2	1	1
3	3	3
4	2	2
...	...	...
353	1	1
354	1	2
355	2	2
356	1	1
357	1	1

358 rows × 2 columns

from bioneuralnet.metrics import evaluate_rf

X_raw = merged_omics.values
y_global = phenotype.values
rf_accuracy = evaluate_rf(X_raw, y_global, n=100, mode="classification")

plot_performance(predictions[1], rf_accuracy, "Raw Omics, vs DPMON Omics")

c:\Users\ramos\Desktop\GitHub\BioNeuralNet\.venv\lib\site-packages\sklearn\base.py:1389: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)

Clustering with CorrelatedLouvain and HybridLouvain

• BioNeuralNet includes internal modules for performing correlated clustering on complex networks.

• These methods modify and extend the traditional community detection by integrating phenotype correlation, allowing users to extract biologically relevant, phenotype-associated modules from any network.

• For more details on how this performed, please visit our Correlated Clustering Methods tab

from bioneuralnet.clustering import CorrelatedLouvain
import networkx as nx

merged_omics = pd.concat([omics1, omics2], axis=1)
G_network = nx.from_pandas_adjacency(global_network)

louvain_instance = CorrelatedLouvain(
    G=G_network,
    B=merged_omics,
    Y=phenotype,
    tune=True
)

louvain_clusters = louvain_instance.run(as_dfs=True)

display(f"Number of Louvain clusters: {len(louvain_clusters)}")

'Number of Louvain clusters: 3'

# Lets comapre these clusters against SmCCNet clusters
from bioneuralnet.metrics import compare_clusters

compare_clusters(louvain_clusters, smccnet_clusters, phenotype, merged_omics)

	Cluster	Louvain Size	Louvain Correlation	SMCCNET Size	SMCCNET Correlation
0	Cluster_1	77	0.654809	67	0.119675
1	Cluster_2	74	-0.130497	63	0.661981
2	Cluster_3	8	0.046296	15	-0.050186

Lets plot the clustered network from correlated louvain

from bioneuralnet.metrics import plot_network
from bioneuralnet.metrics import louvain_to_adjacency

cluster1 = louvain_clusters[0]
cluster2 = louvain_clusters[1]

# Convert Louvain clusters into adjacency matrices
louvain_adj1 = louvain_to_adjacency(cluster1)
louvain_adj2 = louvain_to_adjacency(cluster2)

# Plot using the converted adjacency matrices
cluster1_mapping = plot_network(louvain_adj1, weight_threshold=0.12, show_labels=True, show_edge_weights=False)
display(cluster1_mapping.head())

cluster2_mapping = plot_network(louvain_adj2, weight_threshold=0.12, show_labels=True, show_edge_weights=False)
display(cluster2_mapping.head())

	Omic	Degree
Index
23	Gene_6	5
51	Gene_7	4
21	Gene_1	4
16	Mir_2	3
34	Gene_299	3

	Omic	Degree
Index
13	Gene_464	5
26	Gene_354	3
23	Gene_434	3
31	Gene_422	3
22	Gene_260	3

from bioneuralnet.clustering import  HybridLouvain
import networkx as nx

merged_omics = pd.concat([omics1, omics2], axis=1)
G_network = nx.from_pandas_adjacency(global_network)

hybrid = HybridLouvain(
    G=G_network,
    B=merged_omics,
    Y=phenotype,
    k3=0.2,
    k4=0.8,
    max_iter=3,
    weight="weight",
    tune=False
)
hybrid_result = hybrid.run()
display("Number of clusters:", len(hybrid_result))

Package Version

import bioneuralnet
print("BioNeuralNet version:", bioneuralnet.__version__)

BioNeuralNet version: 1.0