AI used to diagnose and treat genetic diseases.
Description
Step 2: Data Collection & Preprocessing We will need genetic datasets such as: • 1000 Genomes Project (for genetic variants) • ClinVar (for pathogenic mutations) • GTEx (for gene expression) Python Code for Data Loading and Preprocessing Generate a Synthetic Genetic Dataset This dataset will include: . Gene Mutations (Encoded as numerical values) Expression Levels (Simulating gene expression data) Mutation Type (Categorical: Missense, Nonsense, Frameshift) Disease Labels (Binary classification: 0 = No Disease, 1 = Genetic Disease) import pandas as pd import numpy as np # Set random seed for reproducibility np.random.seed(42) # Generate data num_samples = 1000 gene_mutations = np.random.randint(0, 10, num_samples) # 10 different mutation types expression_levels = np.random.uniform(0.1, 10.0, num_samples) # Simulated expression levels mutation_types = np.random.choice(["Missense", "Nonsense", "Frameshift"], num_samples) disease_labels = np.random.choice([0, 1], num_samples) # 0 = No Disease, 1 = Disease # Create DataFrame df = pd.DataFrame({ "Gene_Mutation": gene_mutations, "Expression_Level": expression_levels, "Mutation_Type": mutation_types, "Disease_Label": disease_labels }) # Save to CSV df.to_csv("genetic_data.csv", index=False) print("Synthetic genetic dataset saved as 'genetic_data.csv'.") Gene_Mutation Expression_Level Mutation_Type Disease_Label 0 6 2.634554 Missense 0 1 3 7.288346 Missense 1 2 7 5.970333 Frameshift 0 3 4 1.111905 Frameshift 1 4 6 9.195630 Missense 0 <class 'pandas.core.frame.DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Gene_Mutation 1000 non-null int64 1 Expression_Level 1000 non-null float64 2 Mutation_Type 1000 non-null object 3 Disease_Label 1000 non-null int64 dtypes: float64(1), int64(2), object(1) memory usage: 31.4+ KB None Gene_Mutation Expression_Level Mutation_Type Disease_Label 0 6 2.634554 1 0 1 3 7.288346 1 1 2 7 5.970333 0 0 3 4 1.111905 0 1 4 6 9.195630 1 0 <class 'pandas.core.frame.DataFrame'> RangeIndex: 1000 entries, 0 to 999 Data columns (total 4 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Gene_Mutation 1000 non-null int64 1 Expression_Level 1000 non-null float64 2 Mutation_Type 1000 non-null int32 3 Disease_Label 1000 non-null int64
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Step 1: Define the Problem Understanding how AI can be used to diagnose and treat genetic diseases. This involves: • Identifying datasets containing genetic information (e.g., genomic sequences, mutations, medical records). • Choosing AI/ML models for classification, prediction, or analysis. • Understanding how AI can assist in diagnosing monogenic, polygenic, chromosomal, and mitochondrial disorders. Step 2: Data Collection & Preprocessing We will need genetic datasets such as: • 1000 Genomes Project (for genetic variants) • ClinVar (for pathogenic mutations) • GTEx (for gene expression) Python Code for Data Loading and Preprocessing Generate a Synthetic Genetic Dataset This dataset will include: 1️⃣ Run the Code: Execute the provided Python script to generate genetic_data.csv. This will create a synthetic dataset simulating genetic mutations, expression levels, and disease classification. 2️⃣ Load and Process the Dataset: Once genetic_data.csv is created, we need to preprocess it and train an AI model to classify genetic diseases. import pandas as pd . Gene Mutations (Encoded as numerical values) Expression Levels (Simulating gene expression data) Mutation Type (Categorical: Missense, Nonsense, Frameshift) Disease Labels (Binary classification: 0 = No Disease, 1 = Genetic Disease) .Python Code to Generate and Save a Synthetic Dataset Step 2: Encode Categorical Variables¶ Since the column Mutation_Type contains categorical values (Missense, Nonsense, Frameshift), we need to encode it numerically for AI models. Step 3: Split Data for Training & Testing Step 4: Train a Machine Learning Model We will use a Random Forest Classifier to predict genetic diseases. Step 5: Improve the Model To enhance the AI model for genetic disease classification, we can take several advanced steps: 1️⃣ Add Real-World Genomic Data Instead of using a synthetic dataset, we can incorporate public genomic databases like: 🔬 1000 Genomes Project (link) 🧬 ClinVar (link) 🔍 Ensembl Genome Browser (link) Example: Load Real Genetic Data V2️⃣ Use Deep Learning (Neural Networks)¶ To increase accuracy, we can use TensorFlow/Keras to train a Deep Neural Network (DNN). Install TensorFlow First (if not installed) Train a Deep Learning Model 3️⃣ Apply Feature Engineering . Feature engineering improves the AI model by transforming raw genomic data into meaningful insights. . Example: Generate New Features Next Steps for Improving Genetic Disease Prediction Model Now that we've handled missing data and trained a basic Random Forest Classifier, let's move forward with: ✅ Hyperparameter tuning for better accuracy ✅ Using Deep Learning (Neural Networks) for genetic predictions ✅ Visualizing feature importance to understand genetic risk factors