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About 10,000 papers for “Interaction network”

Interaction network.

Giovanni Feverati (172447), Claire Lesieur (172463) · Figshare · 2015

<p><b>A.</b> x-ray structure of the heptameric co-chaperonin 10 from <i>M. tuberculosis</i> (PDB code: 1HX5) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009897#pone.0009897-Roberts1" target="_blank">[43]</a>. GeminiRegion detects three different regions of the interface of 1HX5, indicated in different colors. <b>B. Section 2 of the GeminiDistances output of the 1HX5 interface.</b> Only the amino acids of the two adjacent chains are indicated. The color code is the same as in 3A. <b>C. Interaction networks of 1HX5.</b> Based on GeminiDistances and GeminiRegion (see methodology), GeminiGraph visualizes each region of the interface as a graph, referred to as an interaction network. 1HX5 has three regions of interface and therefore three different interaction networks, indicated in boxes matching the colors of 3A. The interaction network labels give: first, the stoichiometry, second the PDB code, third the number of the region and last the number of the “framework chemical bonds” according to GeminiDistances. The amino acids that compose the segments of the interface are indicated on two parallel lines. For each segment, the amino acids involved in a chemical bond are symbolized with an “X” whereas the others are symbolized with a dot “.”. The chemical bond that connects two “X” is symbolized by a continuous line, called a link. The number of chemical bonds connecting two amino acids is symbolized by a number on the right of the link and by the thickness of the link. The sequence number of the amino acid is indicated above (or below) the corresponding “X”. <b>D. The 3D structure of the segments that compose the interface regions, as seen on the x-ray structure.</b> Each 3D structure is framed following the same color code of 3A. For each region, the segments of chain A and G are indicated in red and blue backbones, respectively. Some of the amino acid sequence numbers are indicated. The images of the x-ray structures were generated using Rasmol.</p>

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Interaction network

Pintar, Alessandro, Biasio, Alfredo De, Popovic, Matija et al. · Figshare · 2011

<b>Copyright information:</b>Taken from "The intracellular region of Notch ligands: does the tail make the difference?"http://www.biology-direct.com/content/2/1/19Biology Direct 2007;2():19-19.Published online 10 Jul 2007PMCID:PMC1965462. Summary of experimentally verified interactions for the intracellular region of human or rodent Notch ligands. Proteases are shown as octagons, PDZ-containing proteins as hexagons, E3-ubiquitin ligases as diamonds, transcription factors as smoothed squares; the AP1 enhancer element is shown as a square; phosphorylation of Delta-1 by an unknown kinase (Kin) is also shown. Interactions expected by similarity as shown as dotted lines. The graph was drawn using Cytoscape [50].

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Interaction network

Pintar, Alessandro, Biasio, Alfredo De, Popovic, Matija et al. · Figshare · 2011

<b>Copyright information:</b>Taken from "The intracellular region of Notch ligands: does the tail make the difference?"http://www.biology-direct.com/content/2/1/19Biology Direct 2007;2():19-19.Published online 10 Jul 2007PMCID:PMC1965462. Summary of experimentally verified interactions for the intracellular region of human or rodent Notch ligands. Proteases are shown as octagons, PDZ-containing proteins as hexagons, E3-ubiquitin ligases as diamonds, transcription factors as smoothed squares; the AP1 enhancer element is shown as a square; phosphorylation of Delta-1 by an unknown kinase (Kin) is also shown. Interactions expected by similarity as shown as dotted lines. The graph was drawn using Cytoscape [50].

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Interaction network.

Navid Mohammad Mirzaei (8561107), Navid Changizi (12251393), Alireza Asadpoure (12251396) et al. · Figshare · 2022

&lt;p&gt;Diagram of interactions between different cell types and molecules in breast tumors for the mouse model, as it has been modeled in this paper. Variables of the model with their descriptions are given in &lt;a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1009953#pcbi.1009953.t001" target="_blank"&gt;Table 1&lt;/a&gt;.&lt;/p&gt;

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Gene interaction network and pathway interaction network analyses.

Zhong-Hao Ji (4336486), Jian Chen (15340), Wei Gao (2085) et al. · Figshare · 2017

&lt;p&gt;A: For genes that are associated with human diseases and immune pathways, larger dots correspond to higher degrees of participation in the interaction network. Red represents down-regulated genes, and green represents up-regulated genes. B: The pathway interaction network between human diseases and organismal systems. Circular nodes represent pathways, and an arrow between two nodes represents an interaction target between pathways.&lt;/p&gt;

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Gene interaction network and pathway interaction network analyses.

Zhong-Hao Ji (4336486), Jian Chen (15340), Wei Gao (2085) et al. · Figshare · 2017

&lt;p&gt;For genes that are not associated with human diseases, larger dots correspond to higher degrees of participation in the interaction network. Red represents down-regulated genes, and green represents up-regulated genes.&lt;/p&gt;

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Gene interaction network.

Giselle Rocha Pinto (12481597), Viviane Almeida Sarmento (4956715), Paulo Cirino de Carvalho-Filho (12481600) et al. · Figshare · 2022

&lt;p&gt;&lt;i&gt;In silico&lt;/i&gt; protein-protein interaction network of up-regulated genes in patients with oGVHD and patients without GVHD. The nodes between genes represent the protein-protein interaction pairs from the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database. Edges represent protein-protein associations. Actions types: green–activation, blue–binding, black–reaction, red–inhibition, purple–catalysis, yellow–transcriptional regulation. Source: STRING interaction network.&lt;/p&gt;

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Gene Interaction Network:

Abdulaziz M. Al-Swailem (180332), Maher M. Shehata (180299), Faisel M. Abu-Duhier (364738) et al. · Figshare · 2015

&lt;p&gt;Most significant network identified by IPA using 8,405 genes found in camel ESTs shared by human, mouse, rat, and bovine. Molecules involved in two most highly associated functions in the network (“hair and skin development and function” and “renal and urological system development” are shown in light green with related functional annotation.&lt;/p&gt;

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Network interaction criterion.

Spencer Farrell (10743602), Arnold Mitnitski (245051), Kenneth Rockwood (65973) et al. · Figshare · 2022

&lt;p&gt;Criteria for determining robust connections. We show the posterior mean of the network weights {&lt;i&gt;W&lt;/i&gt;&lt;sub&gt;&lt;i&gt;ij&lt;/i&gt;&lt;/sub&gt;} vs. the proportion of the posterior above zero for weights with a positive mean, and below zero for weights with a negative mean. The vertical dashed red line shows the criteria for robust connections (which are used in &lt;a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1009746#pcbi.1009746.g004" target="_blank"&gt;Fig 4&lt;/a&gt;), which is a 99% credible interval around the mean not containing zero. We see that larger weights are all credible, while many smaller weights are not.&lt;/p&gt; &lt;p&gt;(TIF)&lt;/p&gt;

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Reconstructing network interaction.

Ghanim Ullah (249178), Steven J. Schiff (249184) · Figshare · 2015

&lt;p&gt;Measured (A, red) and estimated (B, black) for pyramidal cell. (C) Estimated for interneuron. We used the membrane potential recorded from the pyramidal cell (shown in A, red) to not only reconstruct the full dynamics of the same pyramidal cell (only membrane potential shown in B, black) but also reconstructed the dynamics of the interneuron (only membrane potential shown in C, black). Simultaneously recorded from the IN is shown in (D, red) for comparison. Estimates for intracellular concentration and gating variables , for PC are shown in &lt;a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000776#pcbi.1000776.s002" target="_blank"&gt;Figure S2&lt;/a&gt; and the synaptic variables, , are shown in &lt;a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000776#pcbi.1000776.s003" target="_blank"&gt;Figure S3&lt;/a&gt;. Estimated for IN (E) and PC (F) by assimilating measured from IN (shown in (D)). (D–F) are converses of the simulations in (A–C). That is, In (D–F) we used membrane potential recorded from the interneuron (shown in D, red) to not only reconstruct the full dynamics of the same interneuron (only membrane potential shown in E, black) but also the coupled pyramidal cell (only membrane potential shown in F, black: compare with actual values shown in A, red). Simultaneous membrane potential measurements shown in (A,D) were from a pyramidal cell and OLM interneuron in the hippocampus using simultaneous dual whole cell patch clamp recordings demonstrating the firing interplay between these cells during &lt;i&gt;in vitro&lt;/i&gt; seizures. Data provided by Jokubas Ziburkus. Panels (A,D) are modified from &lt;a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000776#pcbi.1000776-Ziburkus1" target="_blank"&gt;[23]&lt;/a&gt; with permission American Physiological Society.&lt;/p&gt;

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Myocilin interaction network.

Mohamed Diwan M. AbdulHameed (1936330), Gregory J. Tawa (414108), Kamal Kumar (91852) et al. · Figshare · 2015

&lt;p&gt;First neighbors of myocilin (MYOC) in the entire high-confidence human protein-protein interaction (PPI) network.&lt;/p&gt;

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Opinion interaction network

Pengyi Fan, Pei Li, Hui Wang et al. · 2012

Recent empirical studies have discovered that many social networks have heterogeneous relationships, which are signed and weighted relationships between individual nodes. To explore the pattern of opinion dynamics in diverse social networks with heterogeneous relationships, we set up a general agent-based simulation framework named opinion interaction network (OIN), and propose a novel model of opinion dynamics, in which the influence of agents depends on their heterogeneous relationships. Then, by conducting a series of simulations based on OIN, we find that the opinions at steady state depend on the degree of social harmoniousness and average connectivity, and the similar pattern can be observed in the network of Erdös_Rényi, small world and scale free, which illustrates that the topological properties such as short path length, high clustering, and heterogeneous degrees have few effects on opinion dynamics with heterogeneous relationships.

Cited by 6

Protein interaction network.

Diana Marcela Penarete-Vargas (668898), Anaïs Boisson (668899), Serge Urbach (248204) et al. · Figshare · 2015

&lt;p&gt;Protein interaction networks were built in Cytoscape 2.5.1 using the PlasmoID plugin. Three clusters of UA1936-interacting proteins were found based on the following biological process terms: “phospholipid metabolism” depicted in pink, “vesicle-mediated transport” in blue and “transport” in violet. A fourth cluster of unknown or unrelated functions was created and named “miscellaneous”, as depicted in orange. For each protein (node), the PlasmoDB ID accession number is indicated in a colored rectangle followed by the putative name of the gene. Proteins in dark-colored rectangles were found in the three replicated experiments and proteins in light-colored rectangles were found in two replicates. The proteins depicted in ellipses are interacting proteins identified in the PlamsoMap interactome analysis with their respective likelihood scores. The colors on the edges in the network represent: Codes for (black), Interacts with (blue), and Participates in (orange). The p values for the gene ontology terms found to be significantly enriched are presented for genes having those annotations in the biological process.&lt;/p&gt;

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Network interaction-overlaps.

Ivan Iossifov (257884), Raul Rodriguez-Esteban (257885), Ilya Mayzus (233285) et al. · Figshare · 2015

&lt;p&gt;A. Overlap between the human and mouse PL (physical and logical) networks. B. Overlap between the human and mouse P (physical) networks. Interactions in A and B are compared through gene orthology. C. Composition of the &lt;i&gt;union&lt;/i&gt; network (GeneWays human (H70-PL0.9), HPRD network, and GeneWays mouse (M70-PL0.9) orthology).&lt;/p&gt;

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NETWORK-BASED INTERACTION

Alan Dix · 2007

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266About Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266Networks as Enablers . . . . . . . . . . . . . . . . . . . . . . . . . . 267Things that are Only Possible with Networks . . . . . . . 267Remote Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267People . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267Physical Things . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 269Networks as Mediators . . . . . . . . . . . . . . . . . . . . . . . . . 270Issues and Problems Because of Networks . . . . . . . . . 270Network Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . 270Bandwidth and Compression . . . . . . . . . . . . . . . . . . 270Latency and Start-Up . . . . . . . . . . . . . . . . . . . . . . . . . 271Jitter and Buffering . . . . . . . . . . . . . . . . . . . . . . . . . . 271Reliability and Loss, Datagram,and Connection-Based Services . . . . . . . . . . . . . . . . 273Quality of Service (QoS) and Reservation . . . . . . . . . 273Encryption, Authentication,and Digital Signatures . . . . . . . . . . . . . . . . . . . . . . . . 273UI Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274Network Transparency . . . . . . . . . . . . . . . . . . . . . . . 274Delays and Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274Coping Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . 275Timeliness of Feedback/Feedthrough, Pace . . . . . . . 275Race Conditions and InconsistentInterface States . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276Media Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277Interactive Conversation and Action . . . . . . . . . . . . . 277Reliability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278Sound and Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 278Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278Jitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278Broadcast and Prerecorded Media . . . . . . . . . . . . . . 278Public Perception: Ownership, Privacy,and Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279Networks as Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . 279Understanding and Managing Networks . . . . . . . . . . . 279Network Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281Internetworking and Tunneling . . . . . . . . . . . . . . . . 281Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282Addresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282All Together . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283Decentralizing: Peer-Peer and Ad-HocNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283Network Management . . . . . . . . . . . . . . . . . . . . . . . . . 283Network Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . 284Network Confusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 285Exploiting the Limitations: Seamfulnessand Virtual Locality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285The Network Within . . . . . . . . . . . . . . . . . . . . . . . . . . . 285Networks as Platforms . . . . . . . . . . . . . . . . . . . . . . . . . 286Algorithms and Architectures for DistributedInterfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286Accessing Shared Objects . . . . . . . . . . . . . . . . . . . . . . . 286Locking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Replication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287Optimistic Concurrency for SynchronousEditing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287In some ways this chapter could be seen as redundant in an HCI book-surely networks are just an implementation mechanism, a detail below the surface; all that matters are the interfaces that are built on them. On the other hand, networked interfaces, especially the web-and, increasingly, mobile devices-have changed the way we view the world and society. Even those bastions of conservatism, the financial institutions, have found themselves in sea change, and a complete re-structuring of the fundamentals of businesses is just an implementation detail.

Cited by 1

Synthetic interaction network.

Yishai Shimoni (230736), Marc Y. Fink (246780), Soon-gang Choi (246783) et al. · Figshare · 2015

&lt;p&gt;A. The biochemical interaction network for the synthetic network, including the four signaling components (S1–S4), and the 10 early genes they affect (G1–G10); B. The network of functional interactions between the four signaling components in the synthetic network, as inferred by PLACA. The inferred functional interactions convey the correct biochemical network; C. The heat map of the change in gene activity in all genes (X axis), as obtained from a set of simulations where each signaling component (Y axis) was perturbed. The heat map reveals which genes were involved in inferring each functional interaction.&lt;/p&gt;

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