confusion matrix
Definition
A confusion matrix is a performance evaluation tool for classification models that displays the relationship between predicted and actual class labels in a tabular format. In bioinformatics, it quantifies true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) for tasks like disease prediction, protein function classification, or gene expression analysis. The matrix enables calculation of key metrics including sensitivity, specificity, precision, and F1-score. It is essential for assessing machine learning models in genomics, proteomics, and clinical diagnostics, helping researchers understand model accuracy, identify systematic prediction errors, and optimize classification thresholds for biological applications.
Visualize confusion matrix in Nodes Bio
Researchers can visualize confusion matrix results as network graphs where nodes represent predicted versus actual classifications, with edge weights indicating frequency of misclassifications. This network approach reveals systematic patterns in model errors, such as which protein families or disease subtypes are commonly confused. Users can overlay confusion matrix metrics onto biological networks to identify which pathways or gene clusters contribute most to classification accuracy or errors.
Visualization Ideas:
- Classification error networks showing misclassification patterns between biological classes
- Performance metric networks comparing multiple models across different datasets
- Feature importance networks linked to confusion matrix outcomes for different biological categories
Example Use Case
A research team develops a machine learning model to classify cancer subtypes based on gene expression profiles from 500 patient samples. Their confusion matrix reveals 85% accuracy overall, but shows the model frequently misclassifies luminal A and luminal B breast cancer subtypes (high FP rate). By analyzing the confusion patterns, they identify overlapping gene signatures between these subtypes and refine their feature selection to include additional hormone receptor markers, improving classification performance for clinical decision support.