Understanding the complex mechanisms governing basic biological processes requires the
characterisation of regulatory motifs modulating gene expression at transcriptional and …

Motivation: Transcription regulatory protein factors often bind DNA as homo-dimers or hetero-
dimers. Thus they recognize structured DNA motifs that are inverted or direct repeats or …

Introduction. The context--Randomness and models--A bit of biology--Simple models for
biological sequences. Why a model?--Permutation model--Bernoulli model--Introduction to …

Recent developments in the frequent pattern mining framework uses additional measures of
interest to reduce the set of discovered patterns. We introduce a rigorous and efficient …

In the framework of patterns in random texts, the Markov chain embedding techniques
consist of turning the occurrences of a pattern over an order-m Markov sequence into those …

Various computational approaches have been developed for predicting cis-regulatory DNA
elements in prokaryotic genomes. We describe a novel method for predicting transcription …

Background Prediction of secondary structures in the expressed sequences of bacterial
genomes allows to investigate spontaneous folding of the corresponding RNA. This is …

This book represents the most comprehensive and up-to-date collection of information on
the topic of computational molecular biology. Bringing the most recent research into the …

Motivation: The ability to identify complex motifs, ie non-contiguous nucleotide sequences, is
a key feature of modern motif finders. Addressing this problem is extremely important, not …

Abstract The technique of Finite Markov Chain Imbedding (FMCI) is a classical approach to
complex combinatorial problems related to sequences. In order to get efficient algorithms, it …