Several remarks on the metric space of genetic codes.

A genetic code, the mapping from trinucleotide codons to amino acids, can be viewed as a partition on the set of 64 codons. A small set of non-standard genetic codes is known, and these codes can be mathematically compared by their partitions of the codon set. To measure distances between set partitions, this study defines a parameterised family of metric functions that includes Shannon entropy as a special case. Distances were computed for 17 curated genetic codes using four members of the metric function family. With these metric functions, nuclear genetic codes had relatively small inter-code distances, while mitochondrial codes exhibited greater variance. Hierarchical clustering using Ward's algorithm produced a tight grouping of nuclear codes and several distinct clades of mitochondrial codes. This family of functions may be employed in other biological applications involving set partitions, such as analysis of microarray data, gene set enrichment and protein-protein interaction mapping.

A greedy algorithm for supervised discretization.

We present a greedy algorithm for supervised discretization using a metric defined on the space of partitions of a set of objects. This proposed technique is useful for preparing the data for classifiers that require nominal attributes. Experimental work on decision trees and naïve Bayes classifiers confirm the efficacy of the proposed algorithm.

The Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer.

Increasing interest in new pattern recognition methods has been motivated by bioinformatics research. The analysis of gene expression data originated from microarrays constitutes an important application area for classification algorithms and illustrates the need for identifying important predictors. We show that the Goodman-Kruskal coefficient can be used for constructing minimal classifiers for tabular data, and we give an algorithm that can construct such classifiers.