Machine Learning Immunogenicity and Vaccine Response Analysis
Automated Machine Learning Model Building
Perform t-Distributed Stochastic Neighbor Embedding (t-SNE)
Cast All Strings to NA
Perform Density-Based Clustering on t-SNE Results Using DBSCAN
Perform Hierarchical Clustering on t-SNE Results
Perform KNN and Louvain Clustering on t-SNE Results
Apply Mclust Clustering on t-SNE Results
Find Optimal Resolution for Louvain Clustering
Generate a Demo Dataset with Specified Number of Clusters and Overlap
Generate a File Header
Main function to carry out Immunaut Analysis
Check if request variable is Empty
Is Numeric
Pick Best Cluster by Modularity
Pick the Best Clustering Result Based on Multiple Metrics
Pick Best Cluster by Silhouette Score
Select the Best Clustering Based on Weighted Scores: AUROC, Modularity...
Plot Clustered t-SNE Results
Preprocess a Dataset Using Specified Methods
Pre-process and Resample Dataset
Remove Outliers Based on Cluster Information
Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations.
Useful links