The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in
the last century, computer scientists began adapting its principles, in particular natural …

In order to get a better understanding of different types of cancers and to find the possible
biomarkers for diseases, recently, many researchers are analyzing the gene expression …

Background Previous studies on bladder cancer have shown nodal involvement to be an
independent indicator of prognosis and survival. This study aimed at developing an …

Grammatical Evolution (GE) is a Evolutionary Algorithm (EA) that takes inspiration from the
biological evolutionary process to search for solutions to problems. This chapter gives a brief …

Associating functional information with biological sequences remains a challenge for
machine learning methods. The performance of these methods often depends on deriving …

Feature selection on mass spectrometry (MS) data is essential for improving classification
performance and biomarker discovery. The number of MS samples is typically very small …

Developing a QSPR model, which not only captures the influence of reactant structures but
also the solvent effect on reaction rate, is of significance. Such QSPR models will serve as a …

Recently, genetic programming (GP) has been applied to the classification of gene
expression data. In its typical implementation, using training data, a single rule or a single …

Background Discrimination between clinical and environmental strains within many bacterial
species is currently underexplored. Genomic analyses have clearly shown the enormous …

Microarray studies and gene expression analysis have received significant attention over
the last few years and provide many promising avenues towards the understanding of …