bHIVE

B-cell Hybrid Immune Variant Engine

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Introduction

bHIVE is an R package implementing an Artificial Immune Network (AI-Net) algorithm. Inspired by principles of immunology, the bHIVE algorithm evolves a population of "antibodies" to model and analyze datasets. It supports three tasks:

• Clustering: Groups data points based on similarity.
• Classification: Assigns labels to data points based on learned patterns.
• Regression: Predicts continuous outcomes based on input features.

The AI-Net algorithm uses clonal selection and mutation, network suppression, and affinity/distance metrics to iteratively refine the antibody population.

Algorithm Basis

The algorithm operates in the following steps:

1. Affinity Calculation: For a data point x and an antibody a, the affinity is defined as:

Affinity(x, a) = f(x, a; params)

where f is a similarity kernel such as Gaussian (RBF), Laplace, or cosine similarity.

1. Clonal Expansion and Mutation: Top-matching antibodies are cloned and mutated:

a' = a + ε

where ε is sampled from a distribution scaled by affinity and iteration parameters.

1. Network Suppression: Antibodies that are too similar (within a threshold ε) are suppressed to maintain diversity:

Distance(ai, aj) < ε ⇒ Suppress a_j

1. Assignment: Data points are assigned to antibodies using affinity (for classification/regression) or distance (for clustering).

Installation

To install the bHIVE from GitHub, use:

```R

Install bHIVE from GitHub

devtools::install_github("BorchLab/bHIVE") ```

Examples

Clustering

```

Load the Iris dataset

data(iris) X <- as.matrix(iris[, 1:4]) # Use numeric features only

Run bHIVE for clustering

res <- bHIVE(X = X, task = "clustering", nAntibodies = 30, beta = 5, epsilon = 0.01, maxIter = 20, k = 3)

View clustering results

table(res$assignments) ```

Classification

```

Load the Iris dataset

data(iris) X <- as.matrix(iris[, 1:4]) # Use numeric features y <- iris$Species # Classification labels

Run bHIVE for classification

res <- bHIVE(X = X, y = y, task = "classification", nAntibodies = 30, beta = 5, epsilon = 0.01, maxIter = 20, k = 3)

Compare predicted labels with actual labels

table(Predicted = res$assignments, Actual = y) ```

Regression

```

Load the Iris dataset

data(iris) X <- as.matrix(iris[, 2:4]) # Use other features as predictors y <- iris$Sepal.Length # Regression target

Run bHIVE for regression

res <- bHIVE(X = X, y = y, task = "regression", nAntibodies = 30, beta = 5, epsilon = 0.01, maxIter = 20, k = 3)

Compare predicted values with actual values

cor(res$assignments, y) ```

Bug Reports/New Features

If you run into any issues or bugs please submit a GitHub issue with details of the issue.

If possible please include a reproducible example.

Any requests for new features or enhancements can also be submitted as GitHub issues.

Contributing

We welcome contributions to the bHIVE project! To contribute:

• Fork the repository.
• Create a feature branch (git checkout -b feature-branch).
• Commit your changes (git commit -m "Add new feature").
• Push to the branch (git push origin feature-branch).
• Open a pull request.