The network shows the dependencies between disorder symptoms (circular nodes) and the intervention (square node). Blue connections indicate positive relationships, while red ones indicate negative relationships. The width and intensity of the edge reflect the strength of the association. The circles that surround each node are predictability measurements, the more circumference is covered reflects the greater predictability. The contour at the square node is also a measure of predictability and indicates correct classification by the other nodes.</p
<p>Linear regression results of TG, HDL and LDL for each metabolite and transcript <b>(A, C, E)</b>,...
<p><b>A.</b> Each node on the top row of this bipartite graph represents a phenotype. Each square on...
Part 7: Medical IntelligenceInternational audienceThis paper presents a computational network model ...
The nodes represent the 70 symptoms/sub-symptoms of the BPDSI-IV instrument, and the edges reflect p...
<p>Network graph of the correlational relationships between 55 items (symptoms) of the CPRS, which f...
<p>Symptoms are represented as nodes and associations between them as edges. Node colours refer to t...
Objective: Within the network approach to psychopathology, cross-sectional partial correlation netwo...
A network approach to the modelling and the analysis of functional and structural Magnetic Resonance...
<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005203#pone-0005203-g003" ...
In contrast with the latent variable models, network psychometricians have proposed that symptoms co...
We have calculated structural network connectivity values derived from graph theory. We have used SW...
Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impa...
The depicted Bayesian network represents interdependencies between variables learned from prospectiv...
Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clini...
The activation dynamics over time of three simulated within-subjects networks are shown, which suffe...
<p>Linear regression results of TG, HDL and LDL for each metabolite and transcript <b>(A, C, E)</b>,...
<p><b>A.</b> Each node on the top row of this bipartite graph represents a phenotype. Each square on...
Part 7: Medical IntelligenceInternational audienceThis paper presents a computational network model ...
The nodes represent the 70 symptoms/sub-symptoms of the BPDSI-IV instrument, and the edges reflect p...
<p>Network graph of the correlational relationships between 55 items (symptoms) of the CPRS, which f...
<p>Symptoms are represented as nodes and associations between them as edges. Node colours refer to t...
Objective: Within the network approach to psychopathology, cross-sectional partial correlation netwo...
A network approach to the modelling and the analysis of functional and structural Magnetic Resonance...
<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0005203#pone-0005203-g003" ...
In contrast with the latent variable models, network psychometricians have proposed that symptoms co...
We have calculated structural network connectivity values derived from graph theory. We have used SW...
Borderline personality disorder (BPD) is associated with symptoms such as affect dysregulation, impa...
The depicted Bayesian network represents interdependencies between variables learned from prospectiv...
Causal interactions between specific psychiatric symptoms could contribute to the heterogenous clini...
The activation dynamics over time of three simulated within-subjects networks are shown, which suffe...
<p>Linear regression results of TG, HDL and LDL for each metabolite and transcript <b>(A, C, E)</b>,...
<p><b>A.</b> Each node on the top row of this bipartite graph represents a phenotype. Each square on...
Part 7: Medical IntelligenceInternational audienceThis paper presents a computational network model ...