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import numpy as np
import pandas as pd
import gradio as gr
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
import joblib
# Generate more realistic synthetic genetic data
def generate_realistic_genetic_data(n_samples=1000):
np.random.seed(42)
# Define genetic markers associated with different conditions
# Marker ranges based on typical genetic variation patterns
genetic_data = {
'BRCA1_mutation': np.random.choice([0, 1], size=n_samples, p=[0.95, 0.05]), # BRCA1 mutation
'P53_variation': np.random.normal(0.5, 0.1, n_samples), # P53 tumor suppressor variation
'APOE_allele': np.random.choice([2, 3, 4], size=n_samples, p=[0.1, 0.7, 0.2]), # APOE allele types
'DNA_methylation': np.random.beta(2, 5, n_samples), # DNA methylation levels
'telomere_length': np.random.normal(6000, 1000, n_samples), # Telomere length
'CYP2D6_activity': np.random.gamma(2, 2, n_samples), # CYP2D6 enzyme activity
'inflammatory_markers': np.random.exponential(2, n_samples), # Inflammatory markers
'glucose_metabolism': np.random.normal(100, 15, n_samples), # Glucose metabolism
'oxidative_stress': np.random.gamma(3, 1, n_samples), # Oxidative stress levels
'immune_response': np.random.normal(0.7, 0.1, n_samples) # Immune response strength
}
# Create DataFrame
df = pd.DataFrame(genetic_data)
# Generate disease status based on complex interactions
disease_probability = (
0.3 * genetic_data['BRCA1_mutation'] +
0.2 * (genetic_data['P53_variation'] > 0.7) +
0.15 * (genetic_data['APOE_allele'] == 4) +
0.1 * (genetic_data['DNA_methylation'] > 0.6) +
0.05 * (genetic_data['telomere_length'] < 5000) +
0.1 * (genetic_data['CYP2D6_activity'] > 5) +
0.05 * (genetic_data['inflammatory_markers'] > 3) +
0.05 * (genetic_data['glucose_metabolism'] > 120)
)
df['disease'] = (disease_probability > 0.5).astype(int)
return df
# Data preprocessing
def preprocess_data(data):
X = data.drop('disease', axis=1)
y = data['disease']
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
return X_scaled, y, scaler
# Train and evaluate model
def train_and_evaluate_model():
# Generate and preprocess data
print("Generating synthetic genetic data...")
data = generate_realistic_genetic_data(1500)
print("\nData Sample:")
print(data.head())
print("\nData Statistics:")
print(data.describe())
# Preprocess data
X_scaled, y, scaler = preprocess_data(data)
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X_scaled, y, test_size=0.2, random_state=42
)
# Train model
print("\nTraining Random Forest model...")
model = RandomForestClassifier(
n_estimators=100,
max_depth=5,
random_state=42
)
model.fit(X_train, y_train)
# Evaluate model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("\nModel Evaluation:")
print(f"Accuracy: {accuracy:.2f}")
print("\nClassification Report:")
print(classification_report(y_test, y_pred))
# Feature importance
feature_importance = pd.DataFrame({
'feature': data.drop('disease', axis=1).columns,
'importance': model.feature_importances_
})
print("\nFeature Importance:")
print(feature_importance.sort_values('importance', ascending=False))
return model, scaler
# Prediction function
def predict_disease(
brca1_mutation, p53_variation, apoe_allele, dna_methylation,
telomere_length, cyp2d6_activity, inflammatory_markers,
glucose_metabolism, oxidative_stress, immune_response
):
# Create input array
input_data = np.array([
brca1_mutation, p53_variation, apoe_allele, dna_methylation,
telomere_length, cyp2d6_activity, inflammatory_markers,
glucose_metabolism, oxidative_stress, immune_response
]).reshape(1, -1)
# Scale input data
scaled_data = scaler.transform(input_data)
# Make prediction
prediction = model.predict_proba(scaled_data)[0]
return {
"No Disease": float(prediction[0]),
"Disease": float(prediction[1])
}
# Train model and get scaler
print("Training model and preparing interface...")
model, scaler = train_and_evaluate_model()
# Create Gradio interface
iface = gr.Interface(
fn=predict_disease,
inputs=[
gr.Number(label="BRCA1 Mutation (0 or 1)", value=0),
gr.Number(label="P53 Variation (typically 0.3-0.7)", value=0.5),
gr.Number(label="APOE Allele (2, 3, or 4)", value=3),
gr.Number(label="DNA Methylation (0-1)", value=0.4),
gr.Number(label="Telomere Length (typically 4000-8000)", value=6000),
gr.Number(label="CYP2D6 Activity (typically 0-10)", value=4),
gr.Number(label="Inflammatory Markers (typically 0-10)", value=2),
gr.Number(label="Glucose Metabolism (typically 70-130)", value=100),
gr.Number(label="Oxidative Stress (typically 0-10)", value=3),
gr.Number(label="Immune Response (typically 0.5-0.9)", value=0.7)
],
outputs=gr.Label(label="Disease Prediction"),
title="Genetic Disease Prediction System",
description="""This system predicts genetic disease risk based on various genetic markers and biological indicators.
Please input values within the suggested ranges for accurate predictions.""",
examples=[
# High-risk example
[1, 0.8, 4, 0.7, 4800, 6, 4, 125, 5, 0.6],
# Low-risk example
[0, 0.4, 3, 0.3, 6500, 3, 1, 95, 2, 0.8],
# Moderate-risk example
[0, 0.6, 3, 0.5, 5500, 4, 2, 110, 3, 0.7]
]
)
# Launch the interface
iface.launch(share=True) |