Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge

Implements the Multi-Objective Clustering Algorithm Guided by a-Priori Biological Knowledge ('MOC-GaPBK') proposed by Parraga-Alava and others (2018) . The algorithm performs gene clustering using 'NSGA-II' as the underlying multi-objective evolutionary engine, together with Path-Relinking and Pareto Local Search as intensification and diversification strategies. Two versions of the Xie-Beni validity index are used as objective functions, one per distance matrix, so that prior biological knowledge can be incorporated through the second matrix.

moc.gapbk

Multi-Objective Clustering Guided by a-Priori Biological Knowledge.

moc.gapbk implements the MOC-GaPBK algorithm proposed by Parraga-Alava and others (2018). The algorithm couples NSGA-II with Path-Relinking (PR) and Pareto Local Search (PLS) to produce a set of non-dominated clustering solutions over two distance matrices: one usually derived from the data, and a second one that encodes a-priori biological (or domain) knowledge.

Installation

You can install the released version of moc.gapbk from CRAN with:

install.packages("moc.gapbk")

Or the development version from GitHub with:

# install.packages("pak")
pak::pak("jorgeklz/package-moc.gapbk")

Basic example

A minimal end-to-end run on synthetic data:

library(moc.gapbk)

set.seed(2026)

# Toy data: 50 objects (e.g. genes) described by 20 features.
x <- matrix(stats::runif(50 * 20, min = -5, max = 10),
            nrow = 50, ncol = 20)

# Two distance matrices over the same set of objects.
d1 <- as.matrix(stats::dist(x, method = "euclidean"))
d2 <- as.matrix(stats::dist(x, method = "manhattan"))

res <- moc.gapbk(dmatrix1   = d1,
                 dmatrix2   = d2,
                 num_k      = 3,
                 generation = 5,
                 pop_size   = 6)

The result is a list with three components: population, matrix.solutions and clustering.

names(res)
#> [1] "population"       "matrix.solutions" "clustering"

The first solution on the Pareto front, as a named integer vector ready to feed into a validation index or a plot:

head(res$clustering[[1]])
#> 1 2 3 4 5 6 
#> 3 3 2 1 2 1
table(res$clustering[[1]])
#> 
#>  1  2  3 
#>  7 15 28

See vignette("moc-gapbk-intro") for a longer walkthrough, including how to enable intensification and diversification stages with local_search = TRUE.

Algorithm overview

Stage	Component	Role
1	NSGA-II	Multi-objective evolutionary engine
2	Path-Relinking	Intensification between Pareto solutions
3	Pareto Local Search	Diversification across the front

Two versions of the Xie-Beni validity index are used as objective functions, one per distance matrix. Typically dmatrix1 is derived from the data itself and dmatrix2 encodes prior knowledge (for example, GO semantic similarity in gene-clustering applications).

Citation

If you use this package, please cite:

Parraga-Alava, J., Dorn, M., Inostroza-Ponta, M. (2018). A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies. BioData Mining 11(1), 1-16. https://doi.org/10.1186/s13040-018-0178-4

You can also run citation("moc.gapbk") inside R.

Code of conduct

Please note that the moc.gapbk project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.