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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
0.973
0.5217
0.7364
0.2636
0.6792
0.9833
192549
therapeutic application
3
0.88
0.6377
0.7597
0.2403
0.7395
0.9
320046
therapeutic application
2
0.8462
0.7971
0.814
0.186
0.8209
0.8333
459677
therapeutic application
1
0.8169
0.8406
0.814
0.186
0.8286
0.7833
588814
ongoing research
4
0.7447
0.3333
0.598
0.402
0.4605
0.8788
309680
ongoing research
3
0.7432
0.5238
0.6618
0.3382
0.6145
0.8081
482205
ongoing research
2
0.7158
0.6476
0.6863
0.3137
0.68
0.7273
626953
ongoing research
1
0.6724
0.7429
0.6814
0.3186
0.7059
0.6162
746862
diagnostic usage
4
0.8667
0.4588
0.7011
0.2989
0.6
0.9326
294246
diagnostic usage
3
0.8361
0.6
0.7471
0.2529
0.6986
0.8876
422641
diagnostic usage
2
0.7606
0.6353
0.7241
0.2759
0.6923
0.809
579093
diagnostic usage
1
0.6842
0.7647
0.7126
0.2874
0.7222
0.6629
780835
causal interaction
4
0.8793
0.2134
0.4772
0.5228
0.3434
0.9478
383754
causal interaction
3
0.8478
0.3264
0.5308
0.4692
0.4713
0.8955
560695
causal interaction
2
0.8496
0.4728
0.6086
0.3914
0.6075
0.8507
778037
causal interaction
1
0.8306
0.636
0.6836
0.3164
0.7204
0.7687
921642

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