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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

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

107 properties

65554
Number of instances (rows) of the dataset.
29
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.
27
Number of numeric attributes.
2
Number of nominal attributes.
0.19
First quartile of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.42
Mean skewness among attributes of the numeric type.
38453850.56
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
4.52
Second quartile (Median) of kurtosis among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
Entropy of the target attribute values.
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Number of instances belonging to the most frequent class.
-1.01
Minimum kurtosis among attributes of the numeric type.
1.95
Second quartile (Median) of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1040.9
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.92
Second quartile (Median) of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
109382990.48
Maximum of means among attributes of the numeric type.
2733
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
1.03
Second quartile (Median) of standard deviation of attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
-0.25
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
63009
The maximum number of distinct values among attributes of the nominal type.
0.01
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
94.82
Third quartile of kurtosis among attributes of the numeric type.
Average class difference between consecutive instances.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
17.43
Maximum skewness among attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
93.1
Percentage of numeric attributes.
4.12
Third quartile of means among attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1038253561.49
Maximum standard deviation of attributes of the numeric type.
Number of instances belonging to the least frequent class.
6.9
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
8.61
Third quartile of skewness among attributes of the numeric type.
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
89.67
Mean kurtosis among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.52
First quartile of kurtosis among attributes of the numeric type.
3.26
Third quartile of standard deviation of attributes of the numeric type.
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
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
4051229.77
Mean of means among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.18
First quartile of means among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.36
First quartile of skewness among attributes of the numeric type.
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
42621.57
Standard deviation of the number of distinct values among attributes of the nominal type.
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
32871
Average number of distinct values among the attributes of the nominal type.

11 tasks

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