Carlo Vittorio Cannistraci

Learn More
Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis(More)
Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere(More)
MOTIVATION Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when(More)
MOTIVATION Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for(More)
Detecting structure in population genetics and case-control studies is important, as it exposes phenomena such as ecoclines, admixture and stratification. Principal Component Analysis (PCA) is a linear dimension-reduction technique commonly used for this purpose, but it struggles to reveal complex, nonlinear data patterns. In this paper we introduce(More)
Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across the two classes. For instance, modelling the connections between workers and their employers, or electors and parties they vote for, are examples of affiliation networks in social analysis. Ultimately,(More)
Dimensionality reduction is largely and successfully employed for the visualization and discrimination of patterns, hidden in multidimensional proteomics datasets. Principal component analysis (PCA), which is the preferred approach for linear dimensionality reduction, may present serious limitations, in particular when samples are nonlinearly related, as(More)
Complex network topologies and hyperbolic geometry seem specularly connected (Papadopoulos et al. 2012), and one of the most fascinating and challenging problems of recent complex network theory is to map a given network to its hyperbolic space. The Popularity Similarity Optimization (PSO) model represents-at the moment-the climax of this theory(More)
Drugs bind to their target proteins, which interact with downstream effectors and ultimately perturb the transcriptome of a cancer cell. These perturbations reveal information about their source, i.e., drugs' targets. Here, we investigate whether these perturbations and protein interaction networks can uncover drug targets and key pathways. We performed the(More)
∩ ∩ ∩ (,) = Γ() Γ() Γ() Γ() = (,) Γ() Γ() JC x y x y x y CN x y x y should read: ∩ ∪ ∪ (,) = Γ() Γ() Γ() Γ() = (,) Γ() Γ() JC x y x y x y CN x y x y The formulation for CJC ∩ (,) = (,) Γ() Γ() CJC x y CAR x y x y should read: ∪ (,) = (,) Γ() Γ() CJC x y CAR x y x y