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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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The top 10 canonical pathways represented in the reference network. The pathways are ordered by the proportion of genes present in the reference network
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btu659-F2: The top 10 canonical pathways represented in the reference network. The pathways are ordered by the proportion of genes present in the reference network

Mentions: By mapping the nodes from the reference network to the genes from the canonical pathways listed in the Molecular Signature Database v3.1 (Subramanian et al., 2005), we observe a diverse representation of cellular processes. Among the most common were cell growth and survival (EGF, INSULIN, PDGF and RAS), interleukin (IL1R, IL3 and IL4), inflammatory response (NFKB) and cell signaling (MAPK) as shown in Figure 2.Fig. 2.


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)

The top 10 canonical pathways represented in the reference network. The pathways are ordered by the proportion of genes present in the reference network
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btu659-F2: The top 10 canonical pathways represented in the reference network. The pathways are ordered by the proportion of genes present in the reference network
Mentions: By mapping the nodes from the reference network to the genes from the canonical pathways listed in the Molecular Signature Database v3.1 (Subramanian et al., 2005), we observe a diverse representation of cellular processes. Among the most common were cell growth and survival (EGF, INSULIN, PDGF and RAS), interleukin (IL1R, IL3 and IL4), inflammatory response (NFKB) and cell signaling (MAPK) as shown in Figure 2.Fig. 2.

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.

Show MeSH