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Biologically significant sites in a protein may be identified by contrasting the rates of
synonymous (Ks) and non-synonymous (Ka) substitutions. This enables the inference of site …

This review article aims to provide an overview of the ways in which techniques from artificial
intelligence can be usefully employed in bioinformatics, both for modelling biological data …

Technological advancements of the last decades sparked an explosion in the amounts of
data acquired in several scientific fields. In particular, high-throughput technologies have …

Substitution matrices are a key tool in important applications such as identifying sequence
homologies, creating sequence alignments and more recently using evolutionary patterns …

An ability to assign protein function from protein structure is important for structural genomics
consortia. The complex relationship between protein fold and function highlights the …

The exponential growth of sequence data does not necessarily lead to an increase in
knowledge about the functions of genes and their products. Prediction of function using …

Sequence analysis is often the first guide for the prediction of residues in a protein family
that may have functional significance. A few methods have been proposed which use the …

At the intersection between artificial intelligence and statistics, supervised learning allows
algorithms to automatically build predictive models from just observations of a system …

Motivation: The large amount of available annotated Arabidopsis thaliana sequences allows
the induction of splice site prediction models with supervised learning algorithms (see …