Limits...
Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge.

Rhrissorrakrai K, Belcastro V, Bilal E, Norel R, Poussin C, Mathis C, Dulize RH, Ivanov NV, Alexopoulos L, Rice JJ, Peitsch MC, Stolovitzky G, Meyer P, Hoeng J - Bioinformatics (2014)

Bottom Line: Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random.Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges.Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. pmeyerr@us.ibm.com or Julia.Hoeng@pmi.com Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, USA, Philip Morris International R&D, Philip Morris Products S.A., 2000 Neuchâtel, Switzerland, Telethon Institute of Genetics and Medicine, Via Pietro Castellino, 111, 80131 Naples, Italy, ProtATonce Ltd, Scientific Park Lefkippos, Patriarchou Grigoriou & Neapoleos 15343 Ag. Paraskevi, Attiki and National Technical University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece.

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Overview of the STC: (A) Schematic of predictions to be made for each sub-challenge. Each sub-challenge required the prediction of the different sets of responses, indicated in red. (B) Schematic of SC4 to indicate utilization of a provided reference network with species-specific information from the training dataset to generate species-specific networks through the addition and removal of edges. Though cytokine measurements were made available to participants, they were not used in scoring, and for simplicity, were not included in this overview figure
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btu611-F1: Overview of the STC: (A) Schematic of predictions to be made for each sub-challenge. Each sub-challenge required the prediction of the different sets of responses, indicated in red. (B) Schematic of SC4 to indicate utilization of a provided reference network with species-specific information from the training dataset to generate species-specific networks through the addition and removal of edges. Though cytokine measurements were made available to participants, they were not used in scoring, and for simplicity, were not included in this overview figure

Mentions: Hence for SC1, participants were provided with GEx, protein phosphorylation (P) and secreted cytokine (Cy) data from stimuli subset A as training data (Fig. 1A). For testing, participants were asked to predict which proteins showed changes in their phosphorylation status (up- or downregulation is hereafter considered as an activation also stated as a response) for each stimulus in subset B. These predictions were to be reported as confidence values between 0 and 1, where 1 indicated the highest confidence of activation and 0 the lowest confidence. Phosphorylation levels were measured by the Luminex xMAP technology—a bead-based assay where microspheres are coated with antibodies designed to bind specifically to phosphorylated proteins—in primary NRBE cells under growing conditions (see methods).Fig. 1.


Understanding the limits of animal models as predictors of human biology: lessons learned from the sbv IMPROVER Species Translation Challenge.

Rhrissorrakrai K, Belcastro V, Bilal E, Norel R, Poussin C, Mathis C, Dulize RH, Ivanov NV, Alexopoulos L, Rice JJ, Peitsch MC, Stolovitzky G, Meyer P, Hoeng J - Bioinformatics (2014)

Overview of the STC: (A) Schematic of predictions to be made for each sub-challenge. Each sub-challenge required the prediction of the different sets of responses, indicated in red. (B) Schematic of SC4 to indicate utilization of a provided reference network with species-specific information from the training dataset to generate species-specific networks through the addition and removal of edges. Though cytokine measurements were made available to participants, they were not used in scoring, and for simplicity, were not included in this overview figure
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4325540&req=5

btu611-F1: Overview of the STC: (A) Schematic of predictions to be made for each sub-challenge. Each sub-challenge required the prediction of the different sets of responses, indicated in red. (B) Schematic of SC4 to indicate utilization of a provided reference network with species-specific information from the training dataset to generate species-specific networks through the addition and removal of edges. Though cytokine measurements were made available to participants, they were not used in scoring, and for simplicity, were not included in this overview figure
Mentions: Hence for SC1, participants were provided with GEx, protein phosphorylation (P) and secreted cytokine (Cy) data from stimuli subset A as training data (Fig. 1A). For testing, participants were asked to predict which proteins showed changes in their phosphorylation status (up- or downregulation is hereafter considered as an activation also stated as a response) for each stimulus in subset B. These predictions were to be reported as confidence values between 0 and 1, where 1 indicated the highest confidence of activation and 0 the lowest confidence. Phosphorylation levels were measured by the Luminex xMAP technology—a bead-based assay where microspheres are coated with antibodies designed to bind specifically to phosphorylated proteins—in primary NRBE cells under growing conditions (see methods).Fig. 1.

Bottom Line: Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random.Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges.Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species. pmeyerr@us.ibm.com or Julia.Hoeng@pmi.com Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: IBM T.J. Watson Research Center, Computational Biology Center, Yorktown Heights, NY 10003, USA, Philip Morris International R&D, Philip Morris Products S.A., 2000 Neuchâtel, Switzerland, Telethon Institute of Genetics and Medicine, Via Pietro Castellino, 111, 80131 Naples, Italy, ProtATonce Ltd, Scientific Park Lefkippos, Patriarchou Grigoriou & Neapoleos 15343 Ag. Paraskevi, Attiki and National Technical University of Athens, Heroon Polytechniou 9, Zografou 15780, Greece.

Show MeSH
Related in: MedlinePlus