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KEGGMetabolicReactionNetwork

KEGGMetabolicReactionNetwork

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Author: 1. Muhammad Naeem","Centre of Research in Data Engineering(CORDE)","MAJU Islamabad Pakistan(naeems.naeem '@' gmail.com). 2. Sohail Asghar","Director/Associate Professor University Institute of IT PMAS-Arid Agriculture University","Rawalpindi Pakistan","Centre of Research in Data Engineering (CORDE)","(sohail.asghar '@' gmail.com) Source: UCI Please cite: Naeem M,Asghar S, Centre of Research in Data Engineering Islamabad Pakistan, naeems.naeem '@' gmail.com, sohail.asg '@' gmail.com Source: 1. Muhammad Naeem, Centre of Research in Data Engineering(CORDE) & Department of Computer Science, MAJU Islamabad Pakistan(naeems.naeem '@' gmail.com). 2. Sohail Asghar, Director/Associate Professor University Institute of IT PMAS-Arid Agriculture University,Rawalpindi Pakistan, Centre of Research in Data Engineering (CORDE),(sohail.asghar '@' gmail.com) Data Set Information: KEGG Metabolic pathways can be realized into network. Two kinds of network / graph can be formed. These include Reaction Network and Relation Network. In Reaction network, Substrate or Product compound are considered as Node and genes are treated as edge. Whereas in the relation network, Substrate and Product componds are considered as Edges while enzyme and genes are placed as nodes. We tool large number of metabolic pathways from KEGG XML. They were modeled into the graph as described above. With the help of Cytoscape tool, variety of network features were compunted. Attribute Information: a) Pathway text b) Connected Components Integer (min:1, max:39 ) c) Diameter Integer (min:1, max:46 ) d) Radius Integer (min:1, max:13 ) e) Centralization Integer (min:0, max:1 ) f) Shortest Path Integer (min:2, max:23420 ) g) Characteristic Path Length Integer (min:1, [Web Link] ) h) Avg.num.Neighbours real ([Web Link], [Web Link]) i) Density real ([Web Link], max:1) j) Heterogeneity real (min:0, [Web Link]) k) Isolated Nodes Integer (min:0, max:3) l) Number of Self Loops Integer (min:0, max:4) m) Multi-edge Node Pair Integer (min:0, max:220) n) NeighborhoodConnectivity real ([Web Link], [Web Link]) o) NumberOfDirectedEdges real ([Web Link], [Web Link]) p) Stress real (min:0, [Web Link]) q) SelfLoops real (min:0, [Web Link]) r) Partner Of MultiEdged NodePairs Integer (min:0, max:3) s) Degree real (min:1, [Web Link]) t) TopologicalCoefficient real (min:0, max:1) u) BetweennessCentrality real (min:0, [Web Link]) v) Radiality real ([Web Link], max:30744573457 ) w) Eccentricity real ([Web Link], [Web Link]) x) NumberOfUndirectedEdges real (min:0, [Web Link]) y) ClosenessCentrality real ([Web Link], max:1) z) AverageShortestPathLength real ([Web Link], [Web Link] ) aa) ClusteringCoefficient real (min:0, max:1) bb) nodeCount Integer (min:2, max:232) cc) edgeCount Integer (min:1, max:444) Relevant Papers: Shannon,P., Markiel,A., Ozier,O., Baliga,N.S., Wang,J.T.,Ramage,D., Amin,N., Schwikowski,B. and Ideker,T. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498–2504. Citation Request: Naeem M,Asghar S, Centre of Research in Data Engineering Islamabad Pakistan, naeems.naeem '@' gmail.com, sohail.asg '@' gmail.com

29 features

V15numeric1537 unique values
0 missing
V29numeric150 unique values
0 missing
V28numeric84 unique values
0 missing
V27numeric2249 unique values
0 missing
V26numeric9984 unique values
0 missing
V25numeric13842 unique values
0 missing
V24numeric91 unique values
0 missing
V23numeric3471 unique values
0 missing
V22numeric10099 unique values
0 missing
V21numeric9547 unique values
0 missing
V20numeric8649 unique values
0 missing
V19numeric1529 unique values
0 missing
V18numeric858 unique values
0 missing
V17numeric91 unique values
0 missing
V16numeric7234 unique values
0 missing
V1nominal63009 unique values
0 missing
V14numeric7902 unique values
0 missing
V13numeric47 unique values
0 missing
V12numeric5 unique values
0 missing
V11numeric4 unique values
0 missing
V10numeric3998 unique values
0 missing
V9numeric1176 unique values
0 missing
V8numeric801 unique values
0 missing
V7numeric7734 unique values
0 missing
V6numeric838 unique values
0 missing
V5nominal2733 unique values
0 missing
V4numeric13 unique values
0 missing
V3numeric26 unique values
0 missing
V2numeric22 unique values
0 missing
Show all 29 features

107 properties

NumberOfInstances
65554
Number of instances (rows) of the dataset.
NumberOfFeatures
29
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
27
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
AutoCorrelation
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
Entropy of the target attribute values.
DecisionStumpAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
1040.9
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
109382990.48
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
63009
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
17.43
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1038253561.49
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
89.67
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
4051229.77
Mean of means among attributes of the numeric type.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
32871
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
4.42
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
38453850.56
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.01
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2733
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.25
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.01
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
NaiveBayesAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
93.1
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
6.9
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
2.52
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.18
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
1.36
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.19
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
4.52
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1.95
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
1.92
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
1.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
94.82
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
4.12
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
8.61
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
3.26
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
42621.57
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

11 tasks

Clustering on KEGGMetabolicReactionNetwork
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Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
Clustering on KEGGMetabolicReactionNetwork
0 runs - estimation_procedure: 50 times Clustering
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