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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
0.9524
0.5797
0.7597
0.2403
0.7207
0.9667
231656
therapeutic application
3
0.9
0.6522
0.7752
0.2248
0.7563
0.9167
306168
therapeutic application
2
0.9016
0.7971
0.845
0.155
0.8462
0.9
497726
therapeutic application
1
0.8219
0.8696
0.8295
0.1705
0.8451
0.7833
617293
ongoing research
4
0.7447
0.3333
0.598
0.402
0.4605
0.8788
301766
ongoing research
3
0.7432
0.5238
0.6618
0.3382
0.6145
0.8081
468076
ongoing research
2
0.7188
0.6571
0.6912
0.3088
0.6866
0.7273
607719
ongoing research
1
0.6786
0.7238
0.6814
0.3186
0.7005
0.6364
754526
diagnostic usage
4
0.8667
0.4588
0.7029
0.2971
0.6
0.9333
284123
diagnostic usage
3
0.8276
0.5647
0.7314
0.2686
0.6713
0.8889
390988
diagnostic usage
2
0.7703
0.6706
0.7429
0.2571
0.717
0.8111
585323
diagnostic usage
1
0.6842
0.7647
0.7143
0.2857
0.7222
0.6667
752215
causal interaction
4
0.8814
0.2176
0.4799
0.5201
0.349
0.9478
370167
causal interaction
3
0.8478
0.3264
0.5308
0.4692
0.4713
0.8955
539445
causal interaction
2
0.8507
0.477
0.6113
0.3887
0.6113
0.8507
747920
causal interaction
1
0.8306
0.636
0.6836
0.3164
0.7204
0.7687
885606

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