Data
MiceProtein

MiceProtein

in_preparation ARFF Publicly available Visibility: public Uploaded 08-11-2017 by Joaquin Vanschoren
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Author: Clara Higuera, Katheleen J. Gardiner, Krzysztof J. Cios Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Mice+Protein+Expression) - 2015 Please cite: Higuera C, Gardiner KJ, Cios KJ (2015) Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome. PLoS ONE 10(6): e0129126. Expression levels of 77 proteins measured in the cerebral cortex of 8 classes of control and Down syndrome mice exposed to context fear conditioning, a task used to assess associative learning. The data set consists of the expression levels of 77 proteins/protein modifications that produced detectable signals in the nuclear fraction of cortex. There are 38 control mice and 34 trisomic mice (Down syndrome), for a total of 72 mice. In the experiments, 15 measurements were registered of each protein per sample/mouse. Therefore, for control mice, there are 38x15, or 570 measurements, and for trisomic mice, there are 34x15, or 510 measurements. The dataset contains a total of 1080 measurements per protein. Each measurement can be considered as an independent sample/mouse. The eight classes of mice are described based on features such as genotype, behavior and treatment. According to genotype, mice can be control or trisomic. According to behavior, some mice have been stimulated to learn (context-shock) and others have not (shock-context) and in order to assess the effect of the drug memantine in recovering the ability to learn in trisomic mice, some mice have been injected with the drug and others have not. Classes: ``` * c-CS-s: control mice, stimulated to learn, injected with saline (9 mice) * c-CS-m: control mice, stimulated to learn, injected with memantine (10 mice) * c-SC-s: control mice, not stimulated to learn, injected with saline (9 mice) * c-SC-m: control mice, not stimulated to learn, injected with memantine (10 mice) * t-CS-s: trisomy mice, stimulated to learn, injected with saline (7 mice) * t-CS-m: trisomy mice, stimulated to learn, injected with memantine (9 mice) * t-SC-s: trisomy mice, not stimulated to learn, injected with saline (9 mice) * t-SC-m: trisomy mice, not stimulated to learn, injected with memantine (9 mice) ``` The aim is to identify subsets of proteins that are discriminant between the classes. ### Attribute Information: ``` 1 Mouse ID 2..78 Values of expression levels of 77 proteins; the names of proteins are followed by “_n” indicating that they were measured in the nuclear fraction. For example: DYRK1A_n 79 Genotype: control (c) or trisomy (t) 80 Treatment type: memantine (m) or saline (s) 81 Behavior: context-shock (CS) or shock-context (SC) 82 Class: c-CS-s, c-CS-m, c-SC-s, c-SC-m, t-CS-s, t-CS-m, t-SC-s, t-SC-m ``` ### Relevant Papers: Higuera C, Gardiner KJ, Cios KJ (2015) Self-Organizing Feature Maps Identify Proteins Critical to Learning in a Mouse Model of Down Syndrome. PLoS ONE 10(6): e0129126. [Web Link] journal.pone.0129126 Ahmed MM, Dhanasekaran AR, Block A, Tong S, Costa ACS, Stasko M, et al. (2015) Protein Dynamics Associated with Failed and Rescued Learning in the Ts65Dn Mouse Model of Down Syndrome. PLoS ONE 10(3): e0119491.

82 features

class (target)nominal8 unique values
0 missing
MouseID (row identifier)nominal1080 unique values
0 missing
DYRK1A_Nnumeric1077 unique values
3 missing
ITSN1_Nnumeric1076 unique values
3 missing
BDNF_Nnumeric1077 unique values
3 missing
NR1_Nnumeric1077 unique values
3 missing
NR2A_Nnumeric1077 unique values
3 missing
pAKT_Nnumeric1076 unique values
3 missing
pBRAF_Nnumeric1075 unique values
3 missing
pCAMKII_Nnumeric1077 unique values
3 missing
pCREB_Nnumeric1077 unique values
3 missing
pELK_Nnumeric1077 unique values
3 missing
pERK_Nnumeric1077 unique values
3 missing
pJNK_Nnumeric1076 unique values
3 missing
PKCA_Nnumeric1077 unique values
3 missing
pMEK_Nnumeric1077 unique values
3 missing
pNR1_Nnumeric1077 unique values
3 missing
pNR2A_Nnumeric1077 unique values
3 missing
pNR2B_Nnumeric1077 unique values
3 missing
pPKCAB_Nnumeric1077 unique values
3 missing
pRSK_Nnumeric1077 unique values
3 missing
AKT_Nnumeric1077 unique values
3 missing
BRAF_Nnumeric1077 unique values
3 missing
CAMKII_Nnumeric1077 unique values
3 missing
CREB_Nnumeric1073 unique values
3 missing
ELK_Nnumeric1062 unique values
18 missing
ERK_Nnumeric1077 unique values
3 missing
GSK3B_Nnumeric1077 unique values
3 missing
JNK_Nnumeric1077 unique values
3 missing
MEK_Nnumeric1072 unique values
7 missing
TRKA_Nnumeric1075 unique values
3 missing
RSK_Nnumeric1074 unique values
3 missing
APP_Nnumeric1077 unique values
3 missing
Bcatenin_Nnumeric1062 unique values
18 missing
SOD1_Nnumeric1077 unique values
3 missing
MTOR_Nnumeric1077 unique values
3 missing
P38_Nnumeric1075 unique values
3 missing
pMTOR_Nnumeric1077 unique values
3 missing
DSCR1_Nnumeric1077 unique values
3 missing
AMPKA_Nnumeric1075 unique values
3 missing
NR2B_Nnumeric1077 unique values
3 missing
pNUMB_Nnumeric1077 unique values
3 missing
RAPTOR_Nnumeric1077 unique values
3 missing
TIAM1_Nnumeric1075 unique values
3 missing
pP70S6_Nnumeric1076 unique values
3 missing
NUMB_Nnumeric1080 unique values
0 missing
P70S6_Nnumeric1080 unique values
0 missing
pGSK3B_Nnumeric1080 unique values
0 missing
pPKCG_Nnumeric1080 unique values
0 missing
CDK5_Nnumeric1080 unique values
0 missing
S6_Nnumeric1080 unique values
0 missing
ADARB1_Nnumeric1080 unique values
0 missing
AcetylH3K9_Nnumeric1080 unique values
0 missing
RRP1_Nnumeric1080 unique values
0 missing
BAX_Nnumeric1080 unique values
0 missing
ARC_Nnumeric1080 unique values
0 missing
ERBB4_Nnumeric1079 unique values
0 missing
nNOS_Nnumeric1079 unique values
0 missing
Tau_Nnumeric1080 unique values
0 missing
GFAP_Nnumeric1079 unique values
0 missing
GluR3_Nnumeric1080 unique values
0 missing
GluR4_Nnumeric1079 unique values
0 missing
IL1B_Nnumeric1080 unique values
0 missing
P3525_Nnumeric1080 unique values
0 missing
pCASP9_Nnumeric1080 unique values
0 missing
PSD95_Nnumeric1080 unique values
0 missing
SNCA_Nnumeric1079 unique values
0 missing
Ubiquitin_Nnumeric1080 unique values
0 missing
pGSK3B_Tyr216_Nnumeric1080 unique values
0 missing
SHH_Nnumeric1080 unique values
0 missing
BAD_Nnumeric866 unique values
213 missing
BCL2_Nnumeric795 unique values
285 missing
pS6_Nnumeric1080 unique values
0 missing
pCFOS_Nnumeric1005 unique values
75 missing
SYP_Nnumeric1079 unique values
0 missing
H3AcK18_Nnumeric900 unique values
180 missing
EGR1_Nnumeric870 unique values
210 missing
H3MeK4_Nnumeric810 unique values
270 missing
CaNA_Nnumeric1080 unique values
0 missing
Genotypenominal2 unique values
0 missing
Treatmentnominal2 unique values
0 missing
Behaviornominal2 unique values
0 missing

62 properties

1080
Number of instances (rows) of the dataset.
82
Number of attributes (columns) of the dataset.
8
Number of distinct values of the target attribute (if it is nominal).
1396
Number of missing values in the dataset.
528
Number of instances with at least one value missing.
77
Number of numeric attributes.
5
Number of nominal attributes.
3.66
Percentage of binary attributes.
0.07
Second quartile (Median) of standard deviation of attributes of the numeric type.
1
Maximum entropy among attributes.
-0.74
Minimum kurtosis among attributes of the numeric type.
48.89
Percentage of instances having missing values.
1
Third quartile of entropy among attributes.
62.55
Maximum kurtosis among attributes of the numeric type.
0.12
Minimum of means among attributes of the numeric type.
1.58
Percentage of missing values.
2.29
Third quartile of kurtosis among attributes of the numeric type.
3.84
Maximum of means among attributes of the numeric type.
1
Minimal mutual information between the nominal attributes and the target attribute.
93.9
Percentage of numeric attributes.
0.79
Third quartile of means among attributes of the numeric type.
1
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
6.1
Percentage of nominal attributes.
1
Third quartile of mutual information between the nominal attributes and the target attribute.
8
The maximum number of distinct values among attributes of the nominal type.
-0.88
Minimum skewness among attributes of the numeric type.
1
First quartile of entropy among attributes.
0.97
Third quartile of skewness among attributes of the numeric type.
4.74
Maximum skewness among attributes of the numeric type.
0.01
Minimum standard deviation of attributes of the numeric type.
0.21
First quartile of kurtosis among attributes of the numeric type.
0.23
Third quartile of standard deviation of attributes of the numeric type.
1.3
Maximum standard deviation of attributes of the numeric type.
9.72
Percentage of instances belonging to the least frequent class.
0.19
First quartile of means among attributes of the numeric type.
3
Standard deviation of the number of distinct values among attributes of the nominal type.
1
Average entropy of the attributes.
105
Number of instances belonging to the least frequent class.
1
First quartile of mutual information between the nominal attributes and the target attribute.
4.54
Mean kurtosis among attributes of the numeric type.
3
Number of binary attributes.
0.03
First quartile of skewness among attributes of the numeric type.
0.67
Mean of means among attributes of the numeric type.
0.03
First quartile of standard deviation of attributes of the numeric type.
0.99
Average class difference between consecutive instances.
1
Average mutual information between the nominal attributes and the target attribute.
1
Second quartile (Median) of entropy among attributes.
2.99
Entropy of the target attribute values.
0
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0.89
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.08
Number of attributes divided by the number of instances.
3.5
Average number of distinct values among the attributes of the nominal type.
0.38
Second quartile (Median) of means among attributes of the numeric type.
3
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.79
Mean skewness among attributes of the numeric type.
1
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
13.89
Percentage of instances belonging to the most frequent class.
0.16
Mean standard deviation of attributes of the numeric type.
0.46
Second quartile (Median) of skewness among attributes of the numeric type.
150
Number of instances belonging to the most frequent class.
1
Minimal entropy among attributes.

4 tasks

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0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
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