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 Diseases and/or Diagnostics which are related to enzyme classes

 Please choose one of the four different Confidence Levels:
 Confidence Level 1: Precision > 75%, Accuracy > 70%
 Confidence Level 2: Precision > 77%, Accuracy > 70%
 Confidence Level 3: Precision > 85%, Accuracy > 80%
 Confidence Level 4: Precision > 95%, Accuracy > 80%

DRENDA (Disease Related ENzyme information DAtabase) [1]
DRENDA is a new supplement to BRENDA providing disease-related enzyme information on the absence or malfunction of enzymes which have a major influence on the metabolism, regulation, and immunity etc. causing severe diseases. The development of DRENDA focuses on the automatic search of enzyme-disease relations from titles and abstracts of the PubMed database [2] and its classification. This approach is based on a text-mining method, supported by:
  • BRENDA vocabularies (~100 000 items)
  • EC numbers
  • Enzyme names (including synonyms)
  • MeSH terms for diseases and metabolic diorders from the NCBI database (~23 500 terms)
This approach resulted in 0.9 million enzyme-disease combinations extracted from the literature. Further on the enzyme-disease relations are classified into four categories using machine learning methods via Support Vector Machines [3]:
  • causal interaction: if the absence or the malfuction of an enzyme causes a disease
  • therapeutic application: the therapeutic usage of an enzyme as drug target or therapeutic agent is described
  • diagnostic usage: the enzyme is used for a diagnostic approach/analysis tests or the malfunction of an enzyme is detected to diagnose a disease
  • ongoing research: the research about the enzyme-disease relation is still in progress
Enzyme-disease relationships and their classification in BRENDA [1]

Category
Confidence Level
Precision
Recall
Accuracy
Error
F1 Score
Specificity
Entries
therapeutic application
4
0.9535
0.6029
0.7734
0.2266
0.7387
0.9667
238978
therapeutic application
3
0.9231
0.7059
0.8125
0.1875
0.8
0.9333
332069
therapeutic application
2
0.8788
0.8529
0.8594
0.1406
0.8657
0.8667
463377
therapeutic application
1
0.8182
0.9265
0.8516
0.1484
0.869
0.7667
825421
ongoing research
4
0.7429
0.2476
0.5686
0.4314
0.3714
0.9091
229022
ongoing research
3
0.7333
0.5238
0.6569
0.3431
0.6111
0.798
478439
ongoing research
2
0.7083
0.6476
0.6814
0.3186
0.6766
0.7172
621914
ongoing research
1
0.6783
0.7429
0.6863
0.3137
0.7091
0.6263
742573
diagnostic usage
4
0.8636
0.4578
0.7052
0.2948
0.5984
0.9333
293107
diagnostic usage
3
0.7966
0.5663
0.7225
0.2775
0.662
0.8667
402791
diagnostic usage
2
0.7671
0.6747
0.7457
0.2543
0.7179
0.8111
532654
diagnostic usage
1
0.6923
0.759
0.7225
0.2775
0.7241
0.6889
726875
causal interaction
4
0.8857
0.1308
0.4324
0.5676
0.2279
0.9699
250078
causal interaction
3
0.8667
0.2194
0.4784
0.5216
0.3502
0.9398
396954
causal interaction
2
0.8454
0.346
0.5405
0.4595
0.491
0.8872
577773
causal interaction
1
0.8507
0.481
0.6135
0.3865
0.6146
0.8496
774281

Reference:
  • [1] Söhngen,C., Chang,A., Schomburg,D. (2011) Development of a classification scheme for disease-related enzyme information. BMC Bioinformatics, 12, 329.
  • [2] Sayers,E.W., Barrett,T., Benson,D.A., Bolton,E., Bryant,S.H., Canese,K., Chetvernin,V., Church,D.M., Dicuccio,M., Federhen,S., Feolo,M., Fingerman,I.M., Geer, LY, Helmberg,W., Kapustin,Y., Krasnov,S., Landsman,D., Lipman,D.J., Lu,Z., Madden,T.L., Madej,T., Maglott,R., Marchler-Bauer,A., Miller,V., Karsch-Mizrachi,I., Ostell,J., Panchenko,A., Phan,L., Pruitt,K.D., Schuler,G.D., Sequeira,E., Sherry,S.T., Shumway,M., Sirotkin,K., Slotta,D., Souvorov,A., Starchenko,G., Tatusova,T.A., Wagner,L., Wang,Y., Wilbur,W.J., Yaschenko,E., Ye,J. (2012) Database resources of the National Center for Biotechnology Information. Nucleic Acids Res., 40, D13-D25.
  • [3] Joachims T. In: Advances in Kernel Methods - Support Vector Learning. Schölkopf B, Burges C, Smola A, editor. Cambridge, MA: MIT Press; (1999). Making large-Scale SVM Learning Practical; pp. 169-184.
Funding:
This work was supported by SLING: Serving Life-science Information for the Next Generation