Data
kegg_metabolic_directed

kegg_metabolic_directed

active ARFF CC BY 4.0 Visibility: public Uploaded 23-07-2024 by Bruno Belucci Teixeira
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From original source: ----- KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented. Additional 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. Has Missing Values? No ----- Columns with index 0 were deleted from the dataset, usually because they related to some kind of index.

23 features

23 (target)numeric10479 unique values
0 missing
1numeric103 unique values
0 missing
2numeric358 unique values
0 missing
3numeric13 unique values
0 missing
4numeric21 unique values
0 missing
5numeric2 unique values
0 missing
6numeric920 unique values
0 missing
7numeric6066 unique values
0 missing
8numeric2411 unique values
0 missing
9numeric2 unique values
0 missing
10numeric1 unique values
0 missing
11numeric44 unique values
0 missing
12numeric12001 unique values
0 missing
13numeric2469 unique values
0 missing
14numeric4771 unique values
0 missing
15numeric1 unique values
0 missing
16numeric529 unique values
0 missing
17numeric2469 unique values
0 missing
18numeric9484 unique values
0 missing
19numeric2469 unique values
0 missing
20numeric2037 unique values
0 missing
21numeric13595 unique values
0 missing
22numeric12729 unique values
0 missing

19 properties

53413
Number of instances (rows) of the dataset.
23
Number of attributes (columns) of the dataset.
0
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.
23
Number of numeric attributes.
0
Number of nominal attributes.
100
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
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.
0
Percentage of missing values.
0.87
Average class difference between consecutive instances.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: 23
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