9.2 Supervised learning methods (classification and regression)

Supervised learning is a powerful tool in computational biology, using labeled data to train models for classification and regression tasks. These methods can predict protein functions, diagnose diseases, and estimate drug responses, making them invaluable for biological research and medical applications.

From logistic regression to neural networks, various algorithms tackle different problems in biology. Evaluating these models is crucial, using metrics like accuracy and R-squared to ensure reliable predictions and insights in complex biological systems.

Principles of Supervised Learning

Fundamentals of Supervised Learning

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• Supervised learning is a machine learning approach where a model is trained on labeled data, with input features and corresponding output labels, to learn the mapping between inputs and outputs
• The goal of supervised learning is to build a model that can make accurate predictions or decisions on new, unseen data based on the patterns learned from the labeled training data
• The training process involves optimizing the model's parameters to minimize the difference between the predicted outputs and the actual labels, using a loss function to measure the prediction error
• Overfitting occurs when a model learns the noise in the training data, leading to poor generalization on new data
  • Regularization techniques, such as L1 and L2 regularization, can help mitigate overfitting by adding a penalty term to the loss function

Types of Supervised Learning Tasks

• Supervised learning can be divided into two main categories: classification, where the output is a categorical variable, and regression, where the output is a continuous variable
• Classification tasks aim to assign input instances to predefined categories or classes based on their features (disease diagnosis, protein function prediction)
• Regression tasks aim to predict a continuous output variable based on input features (drug response prediction, disease progression estimation)

Classification Algorithms for Biology

Linear and Non-linear Classification Methods

• Logistic regression is a linear classification algorithm that models the probability of an instance belonging to a particular class using the logistic function (sigmoid)
• Support vector machines (SVM) find the optimal hyperplane that maximally separates the classes in a high-dimensional feature space, using kernel functions to transform the data if necessary
• Neural networks, particularly deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can learn complex non-linear relationships between features and classes

Tree-based Classification Algorithms

• Decision trees recursively partition the feature space based on the most informative features, creating a tree-like structure for classification
  • Random forests combine multiple decision trees to improve robustness and reduce overfitting
• Tree-based methods are interpretable and can handle both categorical and continuous features
• Biological applications of classification include disease diagnosis (cancer subtype classification), protein function prediction (enzyme classification), and cell type identification based on gene expression or imaging data

Regression Models for Prediction

Linear and Regularized Regression

• Linear regression is a fundamental regression algorithm that assumes a linear relationship between the input features and the output variable
  • It estimates the coefficients that minimize the sum of squared errors between the predicted and actual values
• Polynomial regression extends linear regression by including higher-order terms of the input features, allowing for modeling non-linear relationships
• Regularized regression methods, such as Ridge regression (L2 regularization) and Lasso regression (L1 regularization), add a penalty term to the loss function to control model complexity and prevent overfitting

Advanced Regression Techniques

• Non-linear regression models, such as decision trees, random forests, and support vector regression (SVR), can capture more complex relationships between features and the output variable
• Neural networks, especially deep learning architectures like multilayer perceptrons (MLPs) and long short-term memory (LSTM) networks, are powerful tools for regression tasks, capable of learning intricate patterns in the data
• Biological applications of regression include predicting drug response (IC50 values), estimating disease progression (survival time), and inferring gene regulatory relationships from expression data

Supervised Learning Model Evaluation

Performance Metrics and Validation Strategies

• Model evaluation is crucial to assess the effectiveness and generalization ability of supervised learning models
• The dataset is typically split into training, validation, and test sets
  • The training set is used to fit the model, the validation set is used for hyperparameter tuning and model selection, and the test set is used for final performance evaluation on unseen data
• Cross-validation techniques, such as k-fold cross-validation and stratified k-fold cross-validation, provide a more robust estimate of model performance by averaging results across multiple train-test splits

Classification Evaluation Metrics

• For classification tasks, common evaluation metrics include accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic (ROC) curve (AUC-ROC)
  • Accuracy measures the overall correctness of predictions, while precision and recall focus on the model's performance for individual classes
  • The F1-score is the harmonic mean of precision and recall, providing a balanced measure of classification performance
  • The ROC curve plots the true positive rate against the false positive rate at various decision thresholds, and the AUC-ROC summarizes the model's ability to discriminate between classes

Regression Evaluation Metrics

• For regression tasks, evaluation metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared)
  • MSE and RMSE measure the average squared difference between predicted and actual values, with RMSE being the square root of MSE
  • MAE measures the average absolute difference between predicted and actual values, providing a more interpretable metric in the original units of the output variable
  • R-squared represents the proportion of variance in the output variable that is predictable from the input features, with values closer to 1 indicating better model performance
• Model interpretability techniques, such as feature importance and partial dependence plots, can provide insights into the relationships learned by the model and help identify the most influential features for prediction