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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
1
0.4853
0.7266
0.2734
0.6535
1
217204
therapeutic application
3
0.95
0.5588
0.75
0.25
0.7037
0.9667
305283
therapeutic application
2
0.8983
0.7794
0.8359
0.1641
0.8346
0.9
489039
therapeutic application
1
0.8243
0.8971
0.8438
0.1563
0.8592
0.7833
878647
ongoing research
4
0.7353
0.2381
0.5637
0.4363
0.3597
0.9091
254714
ongoing research
3
0.7167
0.4095
0.6127
0.3873
0.5212
0.8283
440347
ongoing research
2
0.7273
0.6095
0.6814
0.3186
0.6632
0.7576
624080
ongoing research
1
0.6931
0.6667
0.6765
0.3235
0.6796
0.6869
772500
diagnostic usage
4
0.8667
0.3133
0.6453
0.3547
0.4602
0.9551
212470
diagnostic usage
3
0.8235
0.506
0.7093
0.2907
0.6269
0.8989
455510
diagnostic usage
2
0.7833
0.5663
0.7151
0.2849
0.6573
0.8539
552379
diagnostic usage
1
0.7375
0.7108
0.7384
0.2616
0.7239
0.764
788941
causal interaction
4
1
0.0212
0.374
0.626
0.0415
1
59013
causal interaction
3
0.8506
0.3136
0.5257
0.4743
0.4582
0.9023
645362
causal interaction
2
0.8644
0.4322
0.5935
0.4065
0.5763
0.8797
818405
causal interaction
1
0.8391
0.6186
0.6802
0.3198
0.7122
0.7895
1054966

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