Data
PhosphoproteinChallenge_DREAM3

PhosphoproteinChallenge_DREAM3

active ARFF Publicly available Visibility: public Uploaded 15-03-2022 by Oleksandr Zadorozhnyi
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//Add the description.md of the data file PhosphoproteinChallenge_DREAM3 Human normal and cancer hepatocytes (cell line HepG2s) were treated with 7 stimuli (Table 1a) that are relevant to hepatocyte physiology. For each applied stimulus, 7 selective inhibitors (Table 1b) that block the activity of specific molecules have been applied independently (i.e., only one inhibitor at a time). For each combination of stimulus-inhibitor, the concentration of 17 intracellular phospho-protein molecules (Table 1c) were measured at three time points (0, 30min, 3hours) after stimulation. Also for each combination of stimulus-inhibitor the extra-cellular concentration of 20 cytokines (Table 1d) released by the cells were measured at 3 time points (0, 3hrs, 24hrs) after stimulation. The experimental design is shown schematically in Figure 1, where the data for either a phospho-protein or a cytokine data is exemplified. The data is contained in two spreadsheets, one for the phosphorylation data (PhosphoproteinChallengeDREAM3.csv) and one for the cytokine release data (CytokineChallengeDREAM3.csv). The data is structured according to the following format: in both files the first column contains the cell type (Normal or Cancer), the second column specifies the stimulus, the third column lists the inhibitor, and the fourth column contains the time of data acquisition in minutes. From column 5 to 21, the file PhosphoproteinChallengeDREAM3.csv contains the abundance of the 17 phospho-proteins in arbitrary fluorescence units and in the order given in Table 1c. From column 5 to 24, the file PhosphoproteinChallengeDREAM3.csv contains the abundance of the 20 measured extracellular cytokines in arbitrary fluorescence units and in the order given in Table 1d. The values that have to be predicted have been replaced in the data files by the text: "PREDICT". Useful Information regarding measurements (a) Data integrity / linearity. Significant effort was dedicated to data integrity. The data are reported as arbitrary (fluorescence) units in the range between 0 and 29000. The upper limit (29000) corresponds to the saturation limit of the detector. Experiments were performed in such a way that measurements are as much as possible within the linear range of the detector. In general, data can be considered linear but there are a few cases that measurements are closer to the upper detection limit of ~29000 (e.g. some cJUN and IL8 measurements) where linearity might have been lost. (b) Detection limits/Repeatability. The coefficient of variation for repeated measurements was found to be approx. 8 percent (mostly due to biological error). With our current experimental design the instrument detector can report data with accuracy as low as ~300. For example, changes from 55 fluorescence units (FU) to 110 FU cannot be considered "2 fold increase" because values lie within the noise error of the detector. On the contrary, data from 1000 to 2000 are significant. (c) Inhibitor effects. There are cases in which our inhibitors (i.e. MEKi, p38i, and JNKi) target molecules whose phosphorylation we measure (i.e. MEK12, p38, and JNK). In the case where the inhibitor is present, the phosphorylation state of the corresponding molecule (i.e. phospho-MEK, phospho-p38, and phospho-JNK) should be assumed "absent" and the phosphorylation value should not be used. This known inhibitor effect is more pronounced on the allosteric inhibitors (i.e. the effect of MEK inhibitor on the MEK phosphorylation). The effects of the inhibitors are indirectly corroborated from the phosphorylation state of their downstream targets (i.e. MEK -> ERK, p38 -> HSP27, JNK -> cJUN). Additional data Any additional prior data already present in the literature can be used. This could be especially useful if a model of the network is needed as part of a method to predict the excluded data. About the Data Data generously provided by Peter Sorger, Harvard Medical School / MIT See [Link](http://wiki.c2b2.columbia.edu/dream/data/scripts/DREAM3/)

21 features

Cell_Typestring2 unique values
0 missing
Stimulusstring8 unique values
0 missing
Inhibitorstring8 unique values
0 missing
Time_of_Data_Acquisition_.min.numeric3 unique values
0 missing
AKTstring244 unique values
0 missing
ERK12string213 unique values
0 missing
GSK3string239 unique values
0 missing
Ikbstring220 unique values
0 missing
JNK12string171 unique values
0 missing
p38string178 unique values
0 missing
p70S6string241 unique values
0 missing
p90RSKstring203 unique values
0 missing
STAT3string143 unique values
0 missing
cJUNstring245 unique values
0 missing
CREBstring218 unique values
0 missing
HistH3string197 unique values
0 missing
HSP27string228 unique values
0 missing
IRS1sstring237 unique values
0 missing
MEK12string237 unique values
0 missing
p53string210 unique values
0 missing
STAT6string70 unique values
0 missing

19 properties

272
Number of instances (rows) of the dataset.
21
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
1
Number of numeric attributes.
0
Number of nominal attributes.
0.08
Number of attributes divided by the number of instances.
4.76
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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