Our understanding of the role of the RNA has changed considerably in recent decades. Advances in molecular biology have shown that some so-called non-coding RNAs (ncRNAs) play different roles in several stages of the life of the cell. These ncRNAs are transcribed from DNA, but, unlike messenger RNAs, they do not code for a protein and are functional. It is now a major challenge to find these new ncRNA in sequenced genomes, and several approaches can be used. One of these approaches focuses on the localization of new members of known ncRNA family.

Biclustering is the unsupervised learning task of mining a data matrix for submatrices, known as biclusters, with desirable properties. For instance, the goal can be to find groups of genes that are co-expressed under particular biological conditions. Many biclustering methods do not allow biclusters to overlap; others do, but need to specify how the biclusters interact at the overlapping regions. It is therefore of interest to devise methods that allow flexible, overlapping bicluster structures while not forcing the practitioner to specify bicluster interaction models.

In recent years there has been intense research to find genetic factors that influence common complex traits. The approach that is commonly followed to discover those associations between genetic factors and complex traits such as diseases is to perform a Genome-Wide Association Study (GWAS). It has been pointed out that there is no single marker for disease risk and no single protective marker but, rather, a collection of markers that confer a graded risk of disease.

Recent developments in next-generation sequencing technologies allow constantly increasing throughput and shorter running times while reducing the costs of the sequencing process. This leads to the production of huge amounts of data which raise important computation challenges not only due to the large volume of information but also to the increase of the reads length and sequencing errors. Several assembly and mapping tools have recently been developed for generating assemblies from short, unpaired sequencing reads.

Genetic Programming (GP) is the youngest paradigm inside the Artificial Intelligence field called Evolutionary Computation. Created by John Koza in 1992, it can be regarded as a powerful generalization of Genetic Algorithms, but unfortunately it is still poorly understood outside the GP community. The goal of this tutorial is to provide motivation, intuition and practical advice about GP, along with very few technical details.

In the last years the constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyses. In fact, considering a temporal aspect represents a great advantage to better understand disease progression and treatment results at a molecular level. In this work, we analyse multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β , to which nearly half of the patients reveal a negative response.

In recent years there has been intense research to find genetic factors that influence common complex traits. The approach that is commonly followed to discover those associations between genetic factors and complex traits such as diseases is to perform a Genome-Wide Association Study (GWAS). It has been pointed out that there is no single marker for disease risk and no single protective marker but, rather, a collection of markers that confer a graded risk of disease.

Disease candidate gene prioritization addresses the association of genes with disease susceptibility. Network-based approaches have successfully exploited the connectivity of biological networks to compute a disease-relatedness score between candidate and known disease genes.

Nature is a great designer and problem solver. The theories posit by Darwin, Mendel and all those that contributed for the modern synthesis, based on molecular biology, explained us how this could happen. Some decades ago, computer scientists start proposing computational models, called evolutionary algorithms, based on some of the processes used by nature, in order to solve problems that either do not have an analytical solution or are to costly if we apply exact methods.