TCGA-KIPAN - Binary Cancer Staging

Cohort Summary:

Stage	Methylation	mRNA	miRNA	Clinical
Raw (features x samples)	20,117 x 867	18,272 x 1,020	472 x 1,005	20 x 941
Final Aligned (samples x features)	658 x 20,116	658 x 18,272	658 x 472	658 x 19
After feature selection	633 x 400	633 x 200	633 x 100	633 x 19

Target Definition: Binary cancer stage: Early (Stages I/II, n=417) vs. Late (Stages III/IV, n=216).

Data Source: FireBrowse KIPAN

import pandas as pd
from pathlib import Path
root = Path("/home/vicente/Github/BioNeuralNet/KIPAN")

mirna_raw = pd.read_csv(root/"KIPAN.miRseq_RPKM_log2.txt", sep="\t",index_col=0,low_memory=False)                            
rna_raw = pd.read_csv(root / "KIPAN.uncv2.mRNAseq_RSEM_normalized_log2.txt", sep="\t",index_col=0,low_memory=False)
meth_raw = pd.read_csv(root/"KIPAN.meth.by_mean.data.txt", sep='\t',index_col=0,low_memory=False)
clinical_raw = pd.read_csv(root / "KIPAN.clin.merged.picked.txt",sep="\t", index_col=0, low_memory=False)

# display all shapes and first few rows of each dataset
display(mirna_raw.iloc[:3,:5])
display(mirna_raw.shape)

display(rna_raw.iloc[:3,:5])
display(rna_raw.shape)

display(meth_raw.iloc[:3,:5])
display(meth_raw.shape)

display(clinical_raw.iloc[:3,:5])
display(clinical_raw.shape)

Data Processing Summary

1. Transpose Data: All raw data (miRNA, RNA, etc.) is flipped so rows represent patients and columns represent features.

2. Standardize Patient IDs: Patient IDs in all tables are cleaned to the 12-character TCGA format (e.g., TCGA-AB-1234) for matching.

3. Handle Duplicates: Duplicate patient rows are averaged in the omics data. The first entry is kept for duplicate patients in the clinical data.

4. Find Common Patients: The script identifies the list of patients that exist in all datasets.

5. Extract & Map Target: The pathologic_stage column is pulled from the clinical data and mapped to a binary variable (0 for Early Stage, 1 for Late Stage). Patients without valid stage data are dropped.

6. Final Data Alignment: All data tables are filtered down to only the common list of patients who also have valid staging data.

from bioneuralnet.utils import m_transform

mirna = mirna_raw.T
rna = rna_raw.T
meth = meth_raw.T
clinical = clinical_raw.T

print(f"miRNA (samples, features): {mirna.shape}")
print(f"RNA (samples, features): {rna.shape}")
print(f"Methylation (samples, features): {meth.shape}")
print(f"Clinical (samples, features): {clinical.shape}")

def trim_barcode(idx):
    return idx.to_series().str.slice(0, 12)

# standarized patient IDs across all files
meth.index = trim_barcode(meth.index)
rna.index = trim_barcode(rna.index)
mirna.index = trim_barcode(mirna.index)
clinical.index = clinical.index.str.upper()
clinical.index.name = "Patient_ID"

# convert all data to numeric, coercing errors to NaN
meth = meth.apply(pd.to_numeric, errors='coerce')
rna = rna.apply(pd.to_numeric, errors='coerce')
mirna = mirna.apply(pd.to_numeric, errors='coerce')

# for any duplicate columns in the omics data, we average their values
meth = meth.groupby(meth.index).mean()
rna = rna.groupby(rna.index).mean()
mirna = mirna.groupby(mirna.index).mean()

#drop unwanted columns
clinical.drop(columns=["Composite Element REF"], errors="ignore", inplace=True)
meth.drop(columns=["Composite Element REF"], errors="ignore", inplace=True)

# for any duplicate rows in the clinical data, we keep the first occurrence
clinical = clinical[~clinical.index.duplicated(keep='first')]

# convert beta values to M-values using bioneuralnet utility with small epsilon to avoid log(0)
meth_m = m_transform(meth, eps=1e-7) 

print(f"\nMethylation shape: {meth_m.shape}")
print(f"RNA shape: {rna.shape}")
print(f"miRNA shape: {mirna.shape}")
print(f"Clinical shape: {clinical.shape}")

for df in [meth_m, rna, mirna]:
    df.columns = df.columns.str.replace(r"\?", "unknown_", regex=True)
    df.columns = df.columns.str.replace(r"\|", "_", regex=True)
    df.columns = df.columns.str.replace("-", "_", regex=False)
    df.columns = df.columns.str.replace(r"_+", "_", regex=True)
    df.columns = df.columns.str.strip("_")
    
    df.fillna(df.mean(), inplace=True)

# to see which pateints are common across all data files
common_patients = sorted(list(set(meth_m.index)&set(rna.index)&set(mirna.index)&set(clinical.index)))

print(f"\nFound: {len(common_patients)} patients across all data types.")

# subset to only common patients
X_meth = meth_m.loc[common_patients]
X_rna = rna.loc[common_patients]
X_mirna = mirna.loc[common_patients]
clinical = clinical.loc[common_patients]

# extract target labels from clinical data
raw_stages = clinical['pathologic_stage'].astype(str).str.lower().str.strip()

# map directly to binary: early (0) vs late (1)
stage_mapping = {
    'stage i': 0,
    'stage ii': 0,
    'stage iii': 1,
    'stage iv': 1
}

# apply mapping and drop any patients without a valid stage 
target_labels = raw_stages.map(stage_mapping).dropna().to_frame(name="target")
target_labels['target'] = target_labels['target'].astype(int)
final_patients = sorted(list(set(common_patients) & set(target_labels.index)))

print(f"\nFiltered down to {len(final_patients)} patients with valid staging data.")

# final alignment
X_meth = meth_m.loc[final_patients]
X_rna = rna.loc[final_patients]
X_mirna = mirna.loc[final_patients]
Y_labels = target_labels.loc[final_patients]

clinical_processed = clinical.loc[final_patients].copy()
clinical_processed.drop(columns=['pathologic_stage'], inplace=True, errors='ignore')

print("\nTarget Distribution (0: Early Stage, 1: Late Stage):")
print(Y_labels['target'].value_counts())

display(X_meth.iloc[:3,:5])
display(X_meth.shape)

display(X_rna.iloc[:3,:5])
display(X_rna.shape)

display(X_mirna.iloc[:3,:5])
display(X_mirna.shape)

display(clinical_processed.iloc[:3,:5])
display(clinical_processed.shape)

display(Y_labels.value_counts())

Feature Selection

Unsupervised feature selection was performed across all three omic modalities using Laplacian Score filtering.

from bioneuralnet.utils import laplacian_score

meth_lap = laplacian_score(X_meth, n_keep=400)
rna_lap = laplacian_score(X_rna, n_keep=200)
mirna_lap = laplacian_score(X_mirna, n_keep=100)

import numpy as np
from bioneuralnet.utils import preprocess_clinical

columns_to_drop = [
    'Hybridization REF', 'Composite Element REF', 'value', 
    'tumor_tissue_site', 'date_of_initial_pathologic_diagnosis', 
    'vital_status', 'days_to_death', 'days_to_last_followup', 
    'days_to_last_known_alive', 
    'pathologic_stage', 'pathology_T_stage', 'pathology_N_stage', 'pathology_M_stage',
    'karnofsky_performance_score', 
    'number_pack_years_smoked',
    'year_of_tobacco_smoking_onset'
]

continuous_cols = ['years_to_birth']

clinical_numeric = preprocess_clinical(
    X=clinical_processed,
    scale=True,
    impute=True,
    drop_columns=columns_to_drop,
    continuous_columns=continuous_cols)


print(f"\nNaN count: {clinical_numeric.isna().sum().sum()}")
print(f"\nInf count: {np.isinf(clinical_numeric.values).sum()}")
print(f"\nColumn dtypes:\n{clinical_numeric.dtypes}")
print(clinical_numeric.describe())

Data Availability

To facilitate rapid experimentation and reproduction of our results, the fully processed and feature-selected dataset used in this example has been made available directly within the package.

Users can load this dataset, bypassing all preceding data acquisition, preprocessing, and feature selection steps.

import pandas as pd
from bioneuralnet.datasets import DatasetLoader

tgca_kipan = DatasetLoader("kipan")
display(tgca_kipan.shape)

# The dataset is returned as a dictionary. We extract each file independently based on the name( Key).
X_methylation = tgca_kipan["methylation"]
X_rna = tgca_kipan["rna"]
X_mirna = tgca_kipan["mirna"]
clinical_numeric = tgca_kipan["clinical"]
Y_labels = tgca_kipan["target"]

display(X_methylation.iloc[:3,:5])
display(X_rna.iloc[:3,:5])
display(X_mirna.iloc[:3,:5])
display(clinical_numeric.iloc[:3,:5])
display(Y_labels.iloc[:3,:5])

Reproducibility and Seeding

This utility function propagates the seed to all sources of randomness, including random, numpy, and torch.

from bioneuralnet.utils import set_seed

SEED = 8183
set_seed(SEED)

from bioneuralnet.network import correlation_network, similarity_network

omics_kipan = pd.concat([X_methylation, X_rna, X_mirna], axis=1)

similarity_22 = similarity_network(omics_kipan, k=22)
spearman_12 = correlation_network(omics_kipan, method="spearman", k=12)

from bioneuralnet.network import NetworkAnalyzer

sim_22_analysis = NetworkAnalyzer(similarity_22, source_omics=[X_methylation, X_rna, X_mirna])
sim_22_analysis.basic_statistics(0.0001)
sim_22_analysis.hub_analysis(0.0001)
sim_22_analysis.find_strongest_edges(5)

spear_12_analysis = NetworkAnalyzer(spearman_12, source_omics=[X_methylation, X_rna, X_mirna])
spear_12_analysis.basic_statistics(0.0001)
spear_12_analysis.hub_analysis(0.0001)
spear_12_analysis.find_strongest_edges(5)

# A targeted subset of clinical variables is selected. This can be modified as needed for testing/experiments

clinical_numeric = clinical_numeric.copy()[[
    'radiation_therapy_yes',
    'histological_type_kidney clear cell renal carcinoma',
    'histological_type_kidney papillary renal cell carcinoma',
    'gender_male',
    'ethnicity_not hispanic or latino',
    'years_to_birth',
    'race_white'
]]

print(clinical_numeric.head())

from bioneuralnet.downstream_task import DPMON

output_dir_base = Path("/home/vicente/Github/BioNeuralNet/dpmon_sage_results/kipan")

current_output_dir = output_dir_base / "similarity_22"
current_output_dir.mkdir(parents=True, exist_ok=True)

dpmon_params_base = {
    "adjacency_matrix": similarity_22,
    "omics_list": omics_kipan,
    "phenotype_data": Y_labels,
    "phenotype_col": "target",
    "clinical_data": clinical_numeric,
    "tune_trials" : 20,
    "model": 'SAGE',
    "tune": True, 
    "cv": True,   
    "n_folds": 5,
    "repeat_num": 5,
    "gpu": True,
    "seed": SEED,
    "output_dir": current_output_dir
}

gcn_dpmon = DPMON(**dpmon_params_base)
gcn_predictions, gcn_metrics, gcn_embeddings = gcn_dpmon.run()

from bioneuralnet.downstream_task import DPMON

output_dir_base = Path("/home/vicente/Github/BioNeuralNet/dpmon_sage_results/kipan")

current_output_dir = output_dir_base / "spearman_12"
current_output_dir.mkdir(parents=True, exist_ok=True)

dpmon_params_base = {
    "adjacency_matrix": spearman_12,
    "omics_list": omics_kipan,
    "phenotype_data": Y_labels,
    "phenotype_col": "target",
    "clinical_data": clinical_numeric,
    "tune_trials" : 20,
    "model": 'SAGE',
    "tune": True, 
    "cv": True,   
    "n_folds": 5,
    "repeat_num": 5,
    "gpu": True,
    "seed": SEED,
    "output_dir": current_output_dir
}

gcn_dpmon = DPMON(**dpmon_params_base)
gcn_predictions, gcn_metrics, gcn_embeddings = gcn_dpmon.run()

from bioneuralnet.downstream_task import DPMON

output_dir_base = Path("/home/vicente/Github/BioNeuralNet/dpmon_gat_multiple_results/kipan")
all_results_gat = []

# To test several graphs sequentially. We can loop over the graphs dynamically like this:

comparison_runs = [
    {"name": "similarity_22", "graph": similarity_22, "omics": omics_kipan},
    {"name": "spearman_12", "graph": spearman_12, "omics": omics_kipan},
]

for run_config in comparison_runs:
    graph_name = run_config["name"]
    A_full = run_config["graph"]
    omics = run_config["omics"]
    
    current_output_dir = output_dir_base / graph_name
    current_output_dir.mkdir(parents=True, exist_ok=True)

    # to test a the same two graphs but with a different GNN we only need to update the 'model' parameter
    dpmon_params_base = {
        "adjacency_matrix": A_full,
        "omics_list": omics,
        "phenotype_data": Y_labels,
        "phenotype_col": "target",
        "clinical_data": clinical_numeric,
        "tune_trials" : 20,
        "model": 'GAT', # change to GCN for testing different GNN
        "tune": True, 
        "cv": True,   
        "n_folds": 5,
        "repeat_num":5,
        "gpu": True,
        "seed": SEED,
        "output_dir": current_output_dir
    }
    
    dpmon_tunned = DPMON(**dpmon_params_base)
    predictions_df, metrics, embeddings = dpmon_tunned.run()

    all_results_gat.append({
        "graph": graph_name,
        "predictions": predictions_df,
        "metrics": metrics,
        "embeddings": embeddings
    })

# Once all the experiments are done, we can review the results for each graph configuration:
for res in all_results_gat:
    graph_name = res["graph"]
    graph_metrics = res["metrics"]
    
    acc_row = graph_metrics.loc[graph_metrics['Metric'] == 'Accuracy'].iloc[0]
    f1_macro_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Macro'].iloc[0]
    f1_weighted_row = graph_metrics.loc[graph_metrics['Metric'] == 'F1 Weighted'].iloc[0]
    recall_row = graph_metrics.loc[graph_metrics['Metric'] == 'Recall'].iloc[0]
    precission_row = graph_metrics.loc[graph_metrics['Metric'] == 'Precision'].iloc[0]
    auc_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUC'].iloc[0]
    aupr_row = graph_metrics.loc[graph_metrics['Metric'] == 'AUPR'].iloc[0]
    
    acc_avg, acc_std = acc_row['Average'], acc_row['StdDev']
    f1_macro_avg, f1_macro_std = f1_macro_row['Average'], f1_macro_row['StdDev']
    f1_weighted_avg, f1_weighted_std = f1_weighted_row['Average'], f1_weighted_row['StdDev']
    recall_avg, recall_std = recall_row['Average'], recall_row['StdDev']
    precision_avg, precision_std = precission_row['Average'], precission_row['StdDev']
    auc_avg, auc_std = auc_row['Average'], auc_row['StdDev']
    aupr_avg, aupr_std = aupr_row['Average'], aupr_row['StdDev']

    print(f"\nResults for: {graph_name}")
    print(f"Accuracy (Avg +/- Std): {acc_avg:.4f} +/- {acc_std:.4f}")
    print(f"F1 Macro (Avg +/- Std): {f1_macro_avg:.4f}  +/- {f1_macro_std:.4f}")
    print(f"F1 Weighted (Avg +/- Std): {f1_weighted_avg:.4f} +/- {f1_weighted_std:.4f}")
    print(f"Recall: {recall_avg:.4f} +/- {recall_std:.4f}")
    print(f"Precision: {precision_avg:.4f} +/- {precision_std:.4f}")
    print(f"AUC: {auc_avg:.4f} +/- {auc_std:.4f}")
    print(f"AUPR: {aupr_avg:.4f} +/- {aupr_std:.4f}")

from sklearn.model_selection import StratifiedKFold, ParameterSampler, RepeatedStratifiedKFold
from sklearn.metrics import accuracy_score, f1_score, recall_score, roc_auc_score, average_precision_score, precision_score
from sklearn.preprocessing import StandardScaler, label_binarize
from sklearn.linear_model import LogisticRegression
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline
from sklearn.base import clone
from scipy.stats import loguniform, randint
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier
from sklearn.svm import SVC

SEED = 8183
y = Y_labels["target"]
X = pd.concat([omics_kipan,clinical_numeric],axis=1)

print(f"Successfully created X vector with shape: {X.shape}")
print(f"Successfully created y vector with shape: {y.shape}")

pipe_lr = Pipeline([
    ('scaler', StandardScaler()),
    ('model', LogisticRegression(solver='lbfgs', max_iter=1000, penalty=None, random_state=SEED))
])

pipe_mlp = Pipeline([
    ('scaler', StandardScaler()),
    ('model', MLPClassifier(max_iter=500, early_stopping=True, n_iter_no_change=10, random_state=SEED))
])

pipe_xgb = Pipeline([
    ('scaler', StandardScaler()),
    ('model', XGBClassifier(eval_metric='logloss', tree_method='hist', max_bin=128, random_state=SEED))
])
pipe_rf = Pipeline([
    ('scaler', StandardScaler()),
    ('model', RandomForestClassifier(random_state=SEED))
])

pipe_svm = Pipeline([
    ('scaler', StandardScaler()),
    ('model', SVC(probability=True, random_state=SEED))
])

pipe_dt = Pipeline([
    ('scaler', StandardScaler()),
    ('model', DecisionTreeClassifier(random_state=SEED))
])

params_lr = {'model__penalty': ['l2'], 'model__C': loguniform(1e-4, 1e2)}

params_mlp = {
    'model__hidden_layer_sizes': [(100,), (100, 50), (50, 50)],
    'model__activation': ['relu', 'tanh'],
    'model__alpha': loguniform(1e-5, 1e-1),
    'model__learning_rate_init': loguniform(1e-4, 1e-2)
}
params_xgb = {
    'model__n_estimators': randint(50, 200),
    'model__learning_rate': loguniform(0.01, 0.3),
    'model__max_depth': randint(3, 7),
    'model__subsample': [0.8, 1.0], 
    'model__colsample_bytree': [0.8, 1.0]
}
params_rf = {
    'model__n_estimators': randint(100, 300),
    'model__max_depth': [10, 20, 30, None],
    'model__min_samples_split': randint(2, 10),
    'model__min_samples_leaf': randint(1, 5),
    'model__max_features': ['sqrt', 'log2']
}
params_svm = {
    'model__C': loguniform(1e-2, 1e3),
    'model__kernel': ['rbf', 'linear'],
    'model__gamma': ['scale', 'auto']
}

params_dt = {
    'model__max_depth': randint(3, 15),
    'model__min_samples_split': randint(2, 20),
    'model__criterion': ['gini', 'entropy']
}

models_to_tune = {
    "LogisticRegression": (pipe_lr, params_lr),
    "SVM": (pipe_svm, params_svm),
    "MLP": (pipe_mlp, params_mlp),
    "XGBoost": (pipe_xgb, params_xgb),
    "RandomForest": (pipe_rf, params_rf),
    "DecisionTree": (pipe_dt, params_dt),
}

all_results = {
    "LogisticRegression": {"acc": [], "f1_w": [], "f1_m": [], "recall": [], "auc": [], "precision": [], "aupr": []},
    "MLP": {"acc": [], "f1_w": [], "f1_m": [], "recall": [], "auc": [], "precision": [], "aupr": []},
    "XGBoost": {"acc": [], "f1_w": [], "f1_m": [], "recall": [], "auc": [], "precision": [], "aupr": []},
    "RandomForest": {"acc": [], "f1_w": [], "f1_m": [], "recall": [], "auc": [], "precision": [], "aupr": []},
    "SVM": {"acc": [], "f1_w": [], "f1_m": [], "recall": [], "auc": [], "precision": [], "aupr": []},
    "DecisionTree": {"acc": [], "f1_w": [], "f1_m": [], "recall": [], "auc": [], "precision": [], "aupr": []},
}



for model_name, (pipeline, param_dist) in models_to_tune.items():
    print(f"Evaluating model with nested CV: {model_name}")
    
    outer_cv = RepeatedStratifiedKFold(n_splits=5, n_repeats=5, random_state=SEED)

    # Inner fold loop is for finding the best hyperparameters per fold.
    for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X, y), start=1):
        X_train_outer, X_test_outer = X.iloc[train_idx], X.iloc[test_idx]
        y_train_outer, y_test_outer = y.iloc[train_idx], y.iloc[test_idx]
        inner_cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=SEED)
        
        best_score_fold = -np.inf
        best_params_fold = None

        # A fixed seed = same hyperparamters each fold.
        # No seed = different hyperparameters each fold. 
        # This adds more randomness, and may yield better generalization.
        param_sampler = list(ParameterSampler(param_dist, n_iter=20))
        
        for params in param_sampler:
            inner_scores = []
            
            for inner_train_idx, inner_val_idx in inner_cv.split(X_train_outer, y_train_outer):
                X_train_inner = X_train_outer.iloc[inner_train_idx]
                X_val_inner = X_train_outer.iloc[inner_val_idx]
                y_train_inner = y_train_outer.iloc[inner_train_idx]
                y_val_inner = y_train_outer.iloc[inner_val_idx]
                
                inner_pipeline = clone(pipeline)
                inner_pipeline.set_params(**params)
                inner_pipeline.fit(X_train_inner, y_train_inner)
                
                y_val_pred = inner_pipeline.predict(X_val_inner)
                score = f1_score(y_val_inner, y_val_pred, average='macro', zero_division=0)
                inner_scores.append(score)
            
            mean_score = np.mean(inner_scores)
            if mean_score > best_score_fold:
                best_score_fold = mean_score
                best_params_fold = params
        
        print(f"Outer fold {fold_idx}: best params (inner CV F1-M={best_score_fold:.4f})")
        print(f"{best_params_fold}")

        final_pipeline = clone(pipeline)
        final_pipeline.set_params(**best_params_fold)
        final_pipeline.fit(X_train_outer, y_train_outer)
        
        preds = final_pipeline.predict(X_test_outer)
        
        if hasattr(final_pipeline, "predict_proba"):
            proba = final_pipeline.predict_proba(X_test_outer)
        else:
            proba = None
        
        acc = accuracy_score(y_test_outer, preds)
        f1_w = f1_score(y_test_outer, preds, average='weighted', zero_division=0)
        f1_m = f1_score(y_test_outer, preds, average='macro', zero_division=0)
        recall = recall_score(y_test_outer, preds, average='macro', zero_division=0)
        precision = precision_score(y_test_outer, preds, average='macro', zero_division=0)
        
        auc = np.nan
        aupr = np.nan
        
        if proba is not None:
            try:
                if len(np.unique(y)) == 2:
                    auc = roc_auc_score(y_test_outer, proba[:, 1])
                    aupr = average_precision_score(y_test_outer, proba[:, 1])
                else:
                    auc = roc_auc_score(y_test_outer, proba, multi_class='ovr', average='macro')
                    y_test_bin = label_binarize(y_test_outer, classes=np.unique(y))
                    aupr = average_precision_score(y_test_bin, proba, average='macro')
            except Exception:
                auc = np.nan
                aupr = np.nan

        print(f"Fold {fold_idx} results: Acc={acc:.4f}, F1-W={f1_w:.4f}, "
            f"F1-M={f1_m:.4f}, Recall={recall:.4f}, Precision={precision:.4f}, AUC={auc:.4f}, AUPR={aupr:.4f}")
        
        all_results[model_name]["acc"].append(acc)
        all_results[model_name]["f1_w"].append(f1_w)
        all_results[model_name]["f1_m"].append(f1_m)
        all_results[model_name]["recall"].append(recall)
        all_results[model_name]["precision"].append(precision)
        all_results[model_name]["auc"].append(auc)
        all_results[model_name]["aupr"].append(aupr)

print("\nFINAL BASELINE RESULTS\n")
for model_name, metrics in all_results.items():
    avg_acc = np.mean(metrics["acc"])
    std_acc = np.std(metrics["acc"])
    avg_f1_w = np.mean(metrics["f1_w"])
    std_f1_w = np.std(metrics["f1_w"])
    avg_f1_m = np.mean(metrics["f1_m"])
    std_f1_m = np.std(metrics["f1_m"])
    avg_recall = np.mean(metrics["recall"])
    std_recall = np.std(metrics["recall"])
    avg_prec = np.mean(metrics["precision"])
    std_prec = np.std(metrics["precision"])
    avg_auc = np.nanmean(metrics["auc"])
    std_auc = np.nanstd(metrics["auc"])
    avg_aupr = np.nanmean(metrics["aupr"])
    std_aupr = np.nanstd(metrics["aupr"])
    
    print(f"\n{model_name}:")
    print(f"Accuracy: {avg_acc:.4f} +/- {std_acc:.4f}")
    print(f"Precision: {avg_prec:.4f} +/- {std_prec:.4f}")
    print(f"F1 Weighted: {avg_f1_w:.4f} +/- {std_f1_w:.4f}")
    print(f"F1 Macro: {avg_f1_m:.4f} +/- {std_f1_m:.4f}")
    print(f"Recall: {avg_recall:.4f} +/- {std_recall:.4f}")
    print(f"AUC: {avg_auc:.4f} +/- {std_auc:.4f}")
    print(f"AUPR: {avg_aupr:.4f} +/- {std_aupr:.4f}")