# -*- coding: utf-8 -*- """Untitled17.ipynb Automatically generated by Colab. Original file is located at https://colab.research.google.com/drive/1GwdSjrwh3f6QCzOa8KHr_XkWy0KmZvdV """ import pandas as pd import numpy as np from sklearn.ensemble import GradientBoostingClassifier from sklearn.metrics import roc_auc_score, average_precision_score, precision_recall_curve, f1_score from sklearn.preprocessing import StandardScaler from tqdm.auto import tqdm import warnings warnings.filterwarnings('ignore') ####Leave-Protein-Out Cross-Validation (LPOCV) of the mechanistic pathogenicity model. df_features = pd.read_parquet(PATHS['data_processed'] / 'mutation_features.parquet') df_features['disorder_x_charge'] = df_features['delta_disorder_propensity'] * df_features['delta_charge'] df_features['disorder_x_hydro'] = df_features['delta_disorder_propensity'] * df_features['delta_hydrophobicity'] df_features['ros_x_cysteine'] = df_features['ros_sensitivity'] * df_features['local_cysteine_density'] df_features['propagation_x_disorder'] = df_features['propagation_extent'] * abs(df_features['predicted_delta_disorder_mean']) df_features['disorder_confidence_ratio'] = df_features['predicted_delta_disorder_mean'] / (df_features['predicted_delta_disorder_std'] + 0.01) df_features['abs_delta_disorder'] = abs(df_features['delta_disorder_propensity']) df_features['abs_delta_charge'] = abs(df_features['delta_charge']) df_features['abs_delta_hydro'] = abs(df_features['delta_hydrophobicity']) df_features['is_n_terminal'] = (df_features['position'] < 50).astype(int) df_features['is_c_terminal'] = 0 df_features['is_charge_changing'] = (df_features['delta_charge'] != 0).astype(int) df_features['is_disorder_increasing'] = (df_features['delta_disorder_propensity'] > 0).astype(int) df_features['is_high_ros'] = (df_features['ros_sensitivity'] > 0.5).astype(int) df_features['region_matrix'] = (df_features['region_type'] == 'matrix_idr').astype(int) df_features['region_ims'] = (df_features['region_type'] == 'ims_idr').astype(int) df_features['region_presequence'] = (df_features['region_type'] == 'presequence').astype(int) df_features['region_membrane'] = (df_features['region_type'] == 'membrane_adjacent').astype(int) df_features['has_disorder_annotation'] = df_features['in_disorder_region'].notna() df_features['in_disorder_region'] = df_features['in_disorder_region'].fillna(False) print(f" ✓ {len(df_features)} mutations") print(f" ✓ {df_features['uniprot_acc'].nunique()} protéines uniques") features_mechanistic = [ 'delta_hydrophobicity', 'delta_charge', 'delta_volume', 'delta_disorder_propensity', 'delta_aromatic', 'local_charge_density', 'local_disorder_mean', 'local_disorder_variance', 'local_hydrophobicity', 'local_aromatic_density', 'local_proline_density', 'local_glycine_density', 'local_cysteine_density', 'predicted_delta_disorder_mean', 'predicted_delta_disorder_std', 'propagation_extent', 'max_effective_delta', 'delta_cpr', 'delta_ncpr', 'delta_kappa', 'ros_sensitivity', 'import_efficiency_change', 'cysteine_gained', 'cysteine_lost', 'disulfide_disruption_risk', 'oxidation_sensitivity_change', 'region_matrix', 'region_ims', 'region_presequence', 'region_membrane', 'disorder_x_charge', 'disorder_x_hydro', 'ros_x_cysteine', 'propagation_x_disorder', 'disorder_confidence_ratio', 'abs_delta_disorder', 'abs_delta_charge', 'abs_delta_hydro', 'is_n_terminal', 'is_charge_changing', 'is_disorder_increasing', 'is_high_ros' ] def leave_protein_out_cv(df, feature_cols, model_params, threshold=None): proteins = df['uniprot_acc'].unique() all_y_true = [] all_y_prob = [] all_y_pred = [] protein_results = [] for protein in tqdm(proteins, desc="LPOCV"): train_mask = df['uniprot_acc'] != protein test_mask = df['uniprot_acc'] == protein df_train = df[train_mask] df_test = df[test_mask] if len(df_test) < 2 or df_train['label'].sum() < 5: continue X_train = df_train[feature_cols].fillna(0).values y_train = df_train['label'].values X_test = df_test[feature_cols].fillna(0).values y_test = df_test['label'].values scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) model = GradientBoostingClassifier(**model_params) model.fit(X_train_scaled, y_train) y_prob = model.predict_proba(X_test_scaled)[:, 1] all_y_true.extend(y_test) all_y_prob.extend(y_prob) if y_test.sum() > 0: try: protein_auc = roc_auc_score(y_test, y_prob) except: protein_auc = np.nan protein_results.append({ 'protein': protein, 'n_mutations': len(y_test), 'n_pathogenic': y_test.sum(), 'auc': protein_auc }) all_y_true = np.array(all_y_true) all_y_prob = np.array(all_y_prob) auc_roc = roc_auc_score(all_y_true, all_y_prob) auc_pr = average_precision_score(all_y_true, all_y_prob) if threshold is None: precisions, recalls, thresholds = precision_recall_curve(all_y_true, all_y_prob) f1_scores = 2 * (precisions * recalls) / (precisions + recalls + 1e-10) optimal_idx = np.argmax(f1_scores) threshold = thresholds[optimal_idx] if optimal_idx < len(thresholds) else 0.5 all_y_pred = (all_y_prob >= threshold).astype(int) tp = ((all_y_pred == 1) & (all_y_true == 1)).sum() fp = ((all_y_pred == 1) & (all_y_true == 0)).sum() fn = ((all_y_pred == 0) & (all_y_true == 1)).sum() recall = tp / (tp + fn) if (tp + fn) > 0 else 0 precision = tp / (tp + fp) if (tp + fp) > 0 else 0 return { 'auc_roc': auc_roc, 'auc_pr': auc_pr, 'threshold': threshold, 'recall': recall, 'precision': precision, 'y_true': all_y_true, 'y_prob': all_y_prob, 'protein_results': pd.DataFrame(protein_results) } model_params = { 'n_estimators': 200, 'max_depth': 5, 'learning_rate': 0.05, 'min_samples_split': 10, 'min_samples_leaf': 5, 'subsample': 0.8, 'random_state': 42 } results_A = leave_protein_out_cv(df_features, features_mechanistic, model_params) print(f" AUC-ROC: {results_A['auc_roc']:.4f}") print(f" AUC-PR: {results_A['auc_pr']:.4f}") print(f" Seuil: {results_A['threshold']:.3f}") print(f" Recall: {results_A['recall']:.2%}") print(f" Precision: {results_A['precision']:.2%}") df_protein_results = results_A['protein_results'].dropna() df_protein_results = df_protein_results.sort_values('n_pathogenic', ascending=False) for _, row in df_protein_results.head(10).iterrows(): auc_str = f"{row['auc']:.2f}" if not np.isnan(row['auc']) else "N/A" print(f" {row['protein']}: {row['n_mutations']} mut ({row['n_pathogenic']} patho) - AUC: {auc_str}") validation_results = { 'model_A_mechanistic': { 'features': features_mechanistic, 'auc_roc': results_A['auc_roc'], 'auc_pr': results_A['auc_pr'], 'threshold': results_A['threshold'], 'recall': results_A['recall'], 'precision': results_A['precision'] }, } import json results_path = PATHS['evaluations'] / 'lpocv_results.json' with open(results_path, 'w') as f: json.dump(validation_results, f, indent=2)