Limits...
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.

Show MeSH
The predicted networks for human (A) and rat (B) were compared with the silver standard and against each other using MCC. Only edges present in the reference network were considered
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4325542&req=5

btu659-F3: The predicted networks for human (A) and rat (B) were compared with the silver standard and against each other using MCC. Only edges present in the reference network were considered

Mentions: The heatmaps in Figure 3 show the similarity between predicted networks together with the silver standard using MCC in the space of the reference network edges. Both panels suggest an emerging pattern where a few of the networks are more similar to each other and to the silver standard. The same can be observed when looking at the number of edges that overlaps between the different networks (Supplementary Tables S2 and S3). These are the networks that were ranked higher independent of the scoring metric used (i.e., JS, MCC or TPR-FPR) (Supplementary Table S4).Fig. 3.


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 predicted networks for human (A) and rat (B) were compared with the silver standard and against each other using MCC. Only edges present in the reference network were considered
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

btu659-F3: The predicted networks for human (A) and rat (B) were compared with the silver standard and against each other using MCC. Only edges present in the reference network were considered
Mentions: The heatmaps in Figure 3 show the similarity between predicted networks together with the silver standard using MCC in the space of the reference network edges. Both panels suggest an emerging pattern where a few of the networks are more similar to each other and to the silver standard. The same can be observed when looking at the number of edges that overlaps between the different networks (Supplementary Tables S2 and S3). These are the networks that were ranked higher independent of the scoring metric used (i.e., JS, MCC or TPR-FPR) (Supplementary Table S4).Fig. 3.

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