The Genetics of Myelodysplastic Syndromes: Clinical Relevance.

Myelodysplastic syndromes (MDS) are a clonal disease arising from hematopoietic stem cells, that are characterized by ineffective hematopoiesis (leading to peripheral blood cytopenia) and by an increased risk of evolution into acute myeloid leukemia. MDS are driven by a complex combination of genetic mutations that results in heterogeneous clinical phenotype and outcome. Genetic studies have enabled the identification of a set of recurrently mutated genes which are central to the pathogenesis of MDS and can be organized into a limited number of cellular pathways, including RNA splicing (, , , genes), DNA methylation (, , ), transcription regulation (), signal transduction (, ), DNA repair (), chromatin modification (, ), and cohesin complex ().

Characterization and comparison of gene-centered human interactomes.

The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature.

Classification and Personalized Prognostic Assessment on the Basis of Clinical and Genomic Features in Myelodysplastic Syndromes.

Purpose: Recurrently mutated genes and chromosomal abnormalities have been identified in myelodysplastic syndromes (MDS). We aim to integrate these genomic features into disease classification and prognostication.

Methods: We retrospectively enrolled 2,043 patients.

Network Diffusion Promotes the Integrative Analysis of Multiple Omics.

The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion-also referred to as network propagation-has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity.

Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach.

In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation.

Network-Based Integrative Analysis of Genomics, Epigenomics and Transcriptomics in Autism Spectrum Disorders.

Current studies suggest that autism spectrum disorders (ASDs) may be caused by many genetic factors. In fact, collectively considering multiple studies aimed at characterizing the basic pathophysiology of ASDs, a large number of genes has been proposed. Addressing the problem of molecular data interpretation using gene networks helps to explain genetic heterogeneity in terms of shared pathways.

Network Diffusion-Based Prioritization of Autism Risk Genes Identifies Significantly Connected Gene Modules.

Autism spectrum disorder (ASD) is marked by a strong genetic heterogeneity, which is underlined by the low overlap between ASD risk gene lists proposed in different studies. In this context, molecular networks can be used to analyze the results of several genome-wide studies in order to underline those network regions harboring genetic variations associated with ASD, the so-called "disease modules." In this work, we used a recent network diffusion-based approach to jointly analyze multiple ASD risk gene lists.

Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules.

A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks.

Stochastic neutral modelling of the Gut Microbiota's relative species abundance from next generation sequencing data.

Background: Interest in understanding the mechanisms that lead to a particular composition of the Gut Microbiota is highly increasing, due to the relationship between this ecosystem and the host health state. Particularly relevant is the study of the Relative Species Abundance (RSA) distribution, that is a component of biodiversity and measures the number of species having a given number of individuals. It is the universal behaviour of RSA that induced many ecologists to look for theoretical explanations.

Methods for the integration of multi-omics data: mathematical aspects.

Background: Methods for the integrative analysis of multi-omics data are required to draw a more complete and accurate picture of the dynamics of molecular systems. The complexity of biological systems, the technological limits, the large number of biological variables and the relatively low number of biological samples make the analysis of multi-omics datasets a non-trivial problem.

Results And Conclusions: We review the most advanced strategies for integrating multi-omics datasets, focusing on mathematical and methodological aspects.

Systems medicine of inflammaging.

Systems Medicine (SM) can be defined as an extension of Systems Biology (SB) to Clinical-Epidemiological disciplines through a shifting paradigm, starting from a cellular, toward a patient centered framework. According to this vision, the three pillars of SM are Biomedical hypotheses, experimental data, mainly achieved by Omics technologies and tailored computational, statistical and modeling tools. The three SM pillars are highly interconnected, and their balancing is crucial.