"Implement additional genetic data preprocessing"

[Lykman]

The current genetic data preprocessing pipeline employs basic methods for data cleaning and normalization. However, to enhance the quality of input data for machine learning models and improve prediction accuracy, it is necessary to implement additional preprocessing steps.

Tasks:

• Add additional preprocessing steps such as handling missing values, feature engineering, and outlier removal.
• Investigate and apply appropriate normalization and standardization techniques for genetic data.
• Update the existing data preprocessing scripts to include these new steps.

Why it matters:

Data preprocessing is a crucial step in the machine learning process. Enhancing this step can lead to a significant increase in model accuracy and more precise identification of problematic genes associated with cancer.

Expected Outcome:

Upon completing this task, the quality of the input data is expected to improve, resulting in better machine learning model performance and more reliable analysis outcomes.

[Lykman]

1. Handling Missing Values:

   Implementing K-Nearest Neighbors (KNN) imputation for handling missing values:

   from sklearn.impute import KNNImputer
   
   imputer = KNNImputer(n_neighbors=5)
   data_imputed = imputer.fit_transform(data)

2. Feature Engineering:

   Applying feature scaling (normalization) to ensure consistency across features:

   from sklearn.preprocessing import MinMaxScaler
   
   scaler = MinMaxScaler()
   data_scaled = scaler.fit_transform(data)

   Optionally, performing Principal Component Analysis (PCA) for dimensionality reduction:

   from sklearn.decomposition import PCA
   
   pca = PCA(n_components=0.95)  # Retain 95% of variance
   data_reduced = pca.fit_transform(data_scaled)

3. Outlier Removal:

   Identifying and removing outliers using the Interquartile Range (IQR) method:

   Q1 = data.quantile(0.25)
   Q3 = data.quantile(0.75)
   IQR = Q3 - Q1
   
   data_no_outliers = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis=1)]

4. Normalization and Standardization:

   Applying standardization to the data:

   from sklearn.preprocessing import StandardScaler
   
   scaler = StandardScaler()
   data_standardized = scaler.fit_transform(data)

   Ensuring the same scaler is applied to both training and testing data:

   data_train_scaled = scaler.fit_transform(data_train)
   data_test_scaled = scaler.transform(data_test)

5. Updating Existing Scripts:

   Integrate these steps into the current preprocessing pipeline by updating the existing scripts. Here’s an example of how you might structure the preprocessing steps:

   def preprocess_data(data):
      # Step 1: Handle missing values
      imputer = KNNImputer(n_neighbors=5)
      data = imputer.fit_transform(data)
   
      # Step 2: Feature scaling (normalization)
      scaler = MinMaxScaler()
      data = scaler.fit_transform(data)
   
      # Step 3: Outlier removal
      Q1 = data.quantile(0.25)
      Q3 = data.quantile(0.75)
      IQR = Q3 - Q1
      data = data[~((data < (Q1 - 1.5 * IQR)) | (data > (Q3 + 1.5 * IQR))).any(axis=1)]
   
      # Step 4: Dimensionality reduction (optional)
      pca = PCA(n_components=0.95)
      data = pca.fit_transform(data)
   
      return data

Expected Outcome:

By incorporating these preprocessing steps, we expect to see improved model performance, particularly in terms of accuracy and robustness. The refined input data will enhance the model's ability to accurately identify problematic genes associated with cancer, ultimately contributing to better predictive power and reliability in our analyses.