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A crowd-sourcing approach for the construction of species-specific cell signaling networks.

Bilal E, Sakellaropoulos T, Melas IN, Messinis DE, Belcastro V, Rhrissorrakrai K, Meyer P, Norel R, Iskandar A, Blaese E, Rice JJ, Peitsch MC, Hoeng J, Stolovitzky G, Alexopoulos LG, Poussin C, Challenge Participan - Bioinformatics (2014)

Bottom Line: Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results.Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed.Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: IBM Research, Computational Biology Center, Yorktown Heights, NY 10598, USA, ProtATonce Ltd, Scientific Park Lefkippos, Patriarchou Grigoriou & Neapoleos 15343 Ag. Paraskevi, Attiki, Greece, National Technical University of Athens, Heroon Polytechniou 9, Zografou, 15780, Greece and Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuch√Ętel, Switzerland.

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Panels A and B show two example subnetworks of the consensus network where in blue are human-specific edges, in red rat-specific edges and in black edges common to both species. Depicted in gray are edges from the original reference network that did not gather sufficient consensus between participants. Panel C shows the average consensus score of the edges between a layer and the next one downstream from it for human and rat networks
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btu659-F5: Panels A and B show two example subnetworks of the consensus network where in blue are human-specific edges, in red rat-specific edges and in black edges common to both species. Depicted in gray are edges from the original reference network that did not gather sufficient consensus between participants. Panel C shows the average consensus score of the edges between a layer and the next one downstream from it for human and rat networks

Mentions: Using the threshold determined in the previous section, two consensus networks were built for human and rat using the networks predicted by participants together with the silver standard. The individual edges that resulted are depicted in Supplementary Figure S7 and color-coded based on their presence in the human, rat or both consensus networks. The number and the size of the resulting connected components are listed in Supplementary Table S7. Two of these subnetworks are shown in Figure 5 panels A and B as examples of predicted differences between human and rat cell signaling networks. Although there were plenty of edges that were active only in human or rat, these differences were rather isolated. The differences between human and rat did not scale up to the level of pathways or other higher levels of organization, as will be reinforced in the following analysis.Fig. 5.


A crowd-sourcing approach for the construction of species-specific cell signaling networks.

Bilal E, Sakellaropoulos T, Melas IN, Messinis DE, Belcastro V, Rhrissorrakrai K, Meyer P, Norel R, Iskandar A, Blaese E, Rice JJ, Peitsch MC, Hoeng J, Stolovitzky G, Alexopoulos LG, Poussin C, Challenge Participan - Bioinformatics (2014)

Panels A and B show two example subnetworks of the consensus network where in blue are human-specific edges, in red rat-specific edges and in black edges common to both species. Depicted in gray are edges from the original reference network that did not gather sufficient consensus between participants. Panel C shows the average consensus score of the edges between a layer and the next one downstream from it for human and rat networks
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4325542&req=5

btu659-F5: Panels A and B show two example subnetworks of the consensus network where in blue are human-specific edges, in red rat-specific edges and in black edges common to both species. Depicted in gray are edges from the original reference network that did not gather sufficient consensus between participants. Panel C shows the average consensus score of the edges between a layer and the next one downstream from it for human and rat networks
Mentions: Using the threshold determined in the previous section, two consensus networks were built for human and rat using the networks predicted by participants together with the silver standard. The individual edges that resulted are depicted in Supplementary Figure S7 and color-coded based on their presence in the human, rat or both consensus networks. The number and the size of the resulting connected components are listed in Supplementary Table S7. Two of these subnetworks are shown in Figure 5 panels A and B as examples of predicted differences between human and rat cell signaling networks. Although there were plenty of edges that were active only in human or rat, these differences were rather isolated. The differences between human and rat did not scale up to the level of pathways or other higher levels of organization, as will be reinforced in the following analysis.Fig. 5.

Bottom Line: Such a large network inference challenge not based on synthetic simulations but on real data presented unique difficulties in scoring and interpreting the results.Because any prior knowledge about the networks was already provided to the participants for reference, novel ways for scoring and aggregating the results were developed.Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: IBM Research, Computational Biology Center, Yorktown Heights, NY 10598, USA, ProtATonce Ltd, Scientific Park Lefkippos, Patriarchou Grigoriou & Neapoleos 15343 Ag. Paraskevi, Attiki, Greece, National Technical University of Athens, Heroon Polytechniou 9, Zografou, 15780, Greece and Philip Morris International R&D, Philip Morris Products S.A., Quai Jeanrenaud 5, 2000 Neuch√Ętel, Switzerland.

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