LCS & GBML Central

Clustering issues are fundamental to exploratory analysis of bioinformatics data. This process may follow algorithms that
are reproducible but make assumptions about, for instance, the ability to estimate the global structure by successful local
agglomeration or alternatively, they use pattern recognition methods that are sensitive to the initial conditions. This paper
reviews two clustering methodologies and highlights the differences that result from the changes in data representation, applied
to a protein expression data set for breast cancer (n = 1,076). The two clustering methodologies are a reproducible approach to model-free clustering and a probabilistic competitive
neural network. The results from the two methods are compared with existing studies of the same data set, and the preferred
clustering solutions are profiled for clinical interpretation.

Genomic data, and more generally biomedical data, are often characterized by high dimensionality. An input selection procedure
can attain the two objectives of highlighting the relevant variables (genes) and possibly improving classification results.
In this paper, we propose a wrapper approach to gene selection in classification of gene expression data using simulated annealing
along with supervised classification. The proposed approach can perform global combinatorial searches through the space of
all possible input subsets, can handle cases with numerical, categorical or mixed inputs, and is able to find (sub-)optimal
subsets of inputs giving low classification errors. The method has been tested on publicly available bioinformatics data sets
using support vector machines and on a mixed type data set using classification trees. We also propose some heuristics able
to speed up the convergence. The experimental results highlight the ability of the method to select minimal sets of relevant
features.