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Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge.

Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S - Bioinformatics (2014)

Bottom Line: In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers.The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible.However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality.

View Article: PubMed Central - PubMed

Affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.

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Color-coded visualization of the predictions for  in dataset B: cnaive (upper left panel), cLVQ (upper right) and the combination cAMG of team AMG (lower left). The lower right panel displays the prediction by team IGB. Proteins are numbered according to the list given in Section 2.1
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btu407-F2: Color-coded visualization of the predictions for in dataset B: cnaive (upper left panel), cLVQ (upper right) and the combination cAMG of team AMG (lower left). The lower right panel displays the prediction by team IGB. Proteins are numbered according to the list given in Section 2.1

Mentions: Figure 2 displays the naive, the LVQ based and the combined prediction of team AMG, as well as team IGB’s prediction as color-coded certainties for dataset B.Fig. 2.


Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge.

Biehl M, Sadowski P, Bhanot G, Bilal E, Dayarian A, Meyer P, Norel R, Rhrissorrakrai K, Zeller MD, Hormoz S - Bioinformatics (2014)

Color-coded visualization of the predictions for  in dataset B: cnaive (upper left panel), cLVQ (upper right) and the combination cAMG of team AMG (lower left). The lower right panel displays the prediction by team IGB. Proteins are numbered according to the list given in Section 2.1
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4325536&req=5

btu407-F2: Color-coded visualization of the predictions for in dataset B: cnaive (upper left panel), cLVQ (upper right) and the combination cAMG of team AMG (lower left). The lower right panel displays the prediction by team IGB. Proteins are numbered according to the list given in Section 2.1
Mentions: Figure 2 displays the naive, the LVQ based and the combined prediction of team AMG, as well as team IGB’s prediction as color-coded certainties for dataset B.Fig. 2.

Bottom Line: In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers.The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible.However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality.

View Article: PubMed Central - PubMed

Affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, 9700 AK Groningen, The Netherlands, University of California, Irvine, CA 92617, Department of Physics and Department of Molecular Biology and Biochemistry, Busch Campus, Rutgers University, Piscataway, NJ 08854, IBM T.J. Watson Research Center, Computational Biology, Yorktown Heights, NY 10598, Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA.

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