The biclustering method can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse in gene expression measurement. This is because the biclustering approach, in contrast to the conventional clustering techniques, focuses on finding a subset of the genes and a subset of the experimental conditions that together exhibit coherent behavior. However, the biclustering problem is inherently intractable, and it is often computationally costly to find biclusters with high levels of coherence.

Advanced age represents one of the major risk factors for Parkinson's Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson's Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel the relevance of microRNAs as biomarkers of Parkinson's Disease within the ageing framework.

Mathematical models based on non-linear integral and integro-differential equations are gaining increasing attention in mathematical epidemiology due to their ability to incorporate the past infection dynamic into its current development. This property is particularly suitable to represent the evolution of diseases where the dependence of infectivity on the time since becoming infected plays a crucial role.

Background: Sequencing technologies applied to mammals' microbiomes have revolutionized our understanding of health and disease. Hence, to assess diseases' progression as well as therapies longterm effects, the impact of maladies and drugs on the gut-intestinal (GI) microbiome has to be evaluated.

Background: Variable responses to the Hepatitis B Virus (HBV) vaccine have recently been reported as strongly dependent on genetic causes. Yet, the details on such mechanisms of action are still unknown. In parallel, altered DNA methylation states have been uncovered as important contributors to a variety of health conditions. However, methodologies for the analysis of such high-throughput data (epigenomic), especially from the computational point of view, still lack of a gold standard, mostly due to the intrinsic statistical distribution of methylomic data i.e.

Parkinson's disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens.

Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes.