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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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Overview of the Network Inference Challenge. Participants are provided with a reference network together with Affymetrix gene expression and Luminex phosphoproteomics and cytokine data derived from human and rat bronchial epithelial cells. The goal is to generate two separate networks for human and rat by adding and removing edges from the reference network using the data provided
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btu659-F1: Overview of the Network Inference Challenge. Participants are provided with a reference network together with Affymetrix gene expression and Luminex phosphoproteomics and cytokine data derived from human and rat bronchial epithelial cells. The goal is to generate two separate networks for human and rat by adding and removing edges from the reference network using the data provided

Mentions: Here we present the analysis of the Species-Specific Network Inference challenge, part of the sbv IMPROVER Species Translation set of challenges (https://www.sbvimprover.com). For this challenge, participants were asked to infer human- and rat-specific networks given phosphoprotein, gene expression and cytokine data (Fig. 1). The organizers also provided a common reference network from which participants had to generate the two networks by adding and removing edges. The purpose of this challenge was to augment and refine the reference map in a species-specific manner using data-driven approaches.Fig. 1.


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)

Overview of the Network Inference Challenge. Participants are provided with a reference network together with Affymetrix gene expression and Luminex phosphoproteomics and cytokine data derived from human and rat bronchial epithelial cells. The goal is to generate two separate networks for human and rat by adding and removing edges from the reference network using the data provided
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btu659-F1: Overview of the Network Inference Challenge. Participants are provided with a reference network together with Affymetrix gene expression and Luminex phosphoproteomics and cytokine data derived from human and rat bronchial epithelial cells. The goal is to generate two separate networks for human and rat by adding and removing edges from the reference network using the data provided
Mentions: Here we present the analysis of the Species-Specific Network Inference challenge, part of the sbv IMPROVER Species Translation set of challenges (https://www.sbvimprover.com). For this challenge, participants were asked to infer human- and rat-specific networks given phosphoprotein, gene expression and cytokine data (Fig. 1). The organizers also provided a common reference network from which participants had to generate the two networks by adding and removing edges. The purpose of this challenge was to augment and refine the reference map in a species-specific manner using data-driven approaches.Fig. 1.

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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