KEGG Metabolic Reaction Network (Undirected)
Donated on 11/27/2011
KEGG Metabolic pathways modeled as un-directed reaction network. Variety of graphical features presented.
Dataset Characteristics
Multivariate, Univariate, Text
Subject Area
Biology
Associated Tasks
Classification, Regression, Clustering
Feature Type
Integer, Real
# Instances
65554
# Features
-
Dataset Information
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?
Yes
Introductory Paper
By P. Shannon, Andrew Markiel, Owen Ozier, N. Baliga, Jonathan T. Wang, D. Ramage, Nada Amin, B. Schwikowski, T. Ideker. 2003
Published in Genome Research
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
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| no | |||||
| no | |||||
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0 to 10 of 29
Additional Variable 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, max:15.9729 ) h) Avg.num.Neighbours real (min:0.67, max:3.44) i) Density real (min:0.0082, max:1) j) Heterogeneity real (min:0, max:1.796) 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 (min:0.67, max:15.604) o) NumberOfDirectedEdges real (min:0.67, max:10.22) p) Stress real (min:0, max:2112.05) q) SelfLoops real (min:0, max:0.67) r) Partner Of MultiEdged NodePairs Integer (min:0, max:3) s) Degree real (min:1, max:10.22) t) TopologicalCoefficient real (min:0, max:1) u) BetweennessCentrality real (min:0, max:0.333) v) Radiality real (min:0.5437, max:30744573457 ) w) Eccentricity real (min:0.67, max:25.034) x) NumberOfUndirectedEdges real (min:0, max:0.67) y) ClosenessCentrality real (min:0.1022, max:1) z) AverageShortestPathLength real (min:0.67, max:12.67 ) aa) ClusteringCoefficient real (min:0, max:1) bb) nodeCount Integer (min:2, max:232) cc) edgeCount Integer (min:1, max:444)
Dataset Files
| File | Size |
|---|---|
| Reaction Network (Undirected).data | 11.3 MB |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset kegg_metabolic_reaction_network_undirected = fetch_ucirepo(id=221) # data (as pandas dataframes) X = kegg_metabolic_reaction_network_undirected.data.features y = kegg_metabolic_reaction_network_undirected.data.targets # metadata print(kegg_metabolic_reaction_network_undirected.metadata) # variable information print(kegg_metabolic_reaction_network_undirected.variables)
Naeem, M. & Asghar, S. (2011). KEGG Metabolic Reaction Network (Undirected) [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5G609.
Creators
Muhammad Naeem
Sohail Asghar
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.